Variations in atmospheric CO2 concentration contain information about sources and sinks at the Earth’s surface. Quantitative interpretation of this information by inverse modeling requires a knowledge of the atmospheric transport that links surface exchanges to concentrations in space and time. Such calculations have greatly added to our understanding of the global carbon cycle, yet have been limited by uncertainties in transport and the sparseness of concentration measurements (Gurney et al, 2001). The latter problem may be greatly alleviated over the next decade by the advent of a series of satellite missions to measure atmospheric CO2 globally at high spatial and temporal resolution.
Rayner and O’Brien (2001) demonstrated that ubiquitous, unbiased column measurements could add substantial information to the existing surface flask network even with relatively low precision of about 2 ppmv because of the greatly increased data density. Global low-precision CO2 retrievals from satellites will begin in 2002 with the Atmospheric Infrared Sounder aboard NASA EOS-Aqua, and improve in 2003 with retrievals based on higher spectral resolution data from IASI on Envisat. Engelen et al (2001) showed that AIRS-like spectra could be inverted to determine CO2 concentration to better than 1 ppmv precision on a 4x5 degree global grid in the monthly mean. Both of these instruments measure emission spectra in the thermal infrared, and will therefore be weighted primarily toward the mid-troposphere where spatial structure imposed by surface fluxes is relatively weak due to advection and atmospheric mixing. Dedicated spaceborne CO2 instruments proposed for launch in the middle of the decade would improve on these data using high spectral resolution near-infrared spectrometry of reflected sunlight to retrieve CO2. These retrievals would be naturally weighted by density, emphasizing the lowest few km of the troposphere where spatial gradients are strongest and therefore contain the most information about surface fluxes, but would necessarily be biased because they would only be available during daytime. This limitation could be addressed by active spectrometry using LIDAR. Several missions have been proposed using this technology, any of which would retrieve near-surface concentrations during both day and night, and some of which would retrieve vertical profiles. Pak and Prather (2001) have investigated the use of vertical profiles retrieved from hypothetical satellite measurements on CO2 inversions, and found that they improved flux retrievals relative to column means.
Previous studies of the impact of satellite CO2 observations on the inverse problem have assumed perfect knowledge of atmospheric transport by using the same numerical model for the creation of the satellite “pseudodata” and the Jacobian used in the inversion. In this paper, we investigate the effects of vertical weighting of atmospheric observations and of transport error on flux inversions from hypothetical satellite retrievals of CO2.
