Four-dimensional data assimilation of atmospheric CO2 using AIRS observations



[1] The European Global and regional Earth-system (Atmosphere) Monitoring using Satellite and in situ data (GEMS) project has built a system that is capable of assimilating various sources of satellite and in situ observations to monitor the atmospheric concentrations of CO2 and its surface fluxes. This consists of an atmospheric four-dimensional variational data assimilation system that provides atmospheric fields to a separate variational flux inversion scheme. In this paper, we describe the atmospheric data assimilation system that currently uses radiance observations from the Atmospheric Infrared Sounder (AIRS) to constrain the CO2 mixing ratios of the data assimilation model. We present the CO2 transport model, the bias correction of the observation-model mismatch, and the estimation of the background error covariance matrix. Data assimilation results are compared to independent CO2 observations from NOAA/ESRL aircraft showing a reduction of the mean difference of up to 50% depending on the altitude of the aircraft observations relative to an unconstrained transport model simulation. In the coming years, observations from dedicated CO2 satellite missions will be added to the system. Together with improved error characterization and bias correction, we hope to show that satellite observations can indeed complement the in situ observation system to get a better estimate of global carbon fluxes.