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We present a global chemical data assimilation system using a globalatmosphere model, the Community Atmosphere Model (CAM3) with simplifiedchemistry and the Data Assimilation Research Testbed (DART) assimilationpackage. DART is a community software facility for assimilation studiesusing the ensemble Kalman filter approach. Here, we apply the assimilationsystem to constrain global tropospheric carbon monoxide (CO) by assimilatingmeteorological observations of temperature and horizontal wind velocity andsatellite CO retrievals from the Measurement of Pollution in the Troposphere(MOPITT) satellite instrument. We verify the system performance usingindependent CO observations taken on board the NSF/NCAR C-130 and NASA DC-8aircrafts during the April 2006 part of the Intercontinental ChemicalTransport Experiment (INTEX-B). Our evaluations show that MOPITT dataassimilation provides significant improvements in terms of capturing theobserved CO variability relative to no MOPITT assimilation (i.e. thecorrelation improves from 0.62 to 0.71, significant at 99% confidence).The assimilation provides evidence of median CO loading of about 150 ppbv at700 hPa over the NE Pacific during April 2006. This is marginally higherthan the modeled CO with no MOPITT assimilation (~140 ppbv). Ourensemble-based estimates of model uncertainty also show model overpredictionover the source region (i.e. China) and underprediction over the NE Pacific,suggesting model errors that cannot be readily explained by emissions alone.These results have important implications for improving regional chemicalforecasts and for inverse modeling of CO sources and further demonstrate theutility of the assimilation system in comparing non-coincident measurements,e.g. comparing satellite retrievals of CO with in-situ aircraftmeasurements

Evaluating model performance of an ensemble-based chemical data assimilation system during INTEX-B field mission