Estimating Uncertainty in Atmospheric Trace Gas Measurements for use in Carbon Cycle Inverse Model Studies

The NOAA CMDL Carbon Cycle Greenhouse Gases (CCGG) Group operates an extensive observational network to monitor the abundance of atmospheric trace gases important to understanding the global carbon cycle. Continuous and discrete measurements of these gases (CO₂, CH₄, CO, H₂, N₂O, SF₆, and the stable isotopes of CO₂ and CH₄) from surface sites, towers, aircraft, and voluntary observing ships constitute the most extensive set of atmospheric greenhouse gas observations that are internally consistent with respect to calibration and methodology. CCGG data are frequently used either by themselves or combined with similar observations made by other measurement laboratories to constrain emission estimates derived from carbon cycle inverse models. At present, model transport may be the dominant source of error in these estimates but uncertainty in the observational data also contributes to the overall uncertainty. Including estimates of measurement uncertainty with the observational data may improve the ability to quantify errors in model transport due to topography, circulation, and resolution. Measurement errors can be introduced during the collection, storage, and analysis of atmospheric air samples. Errors may also be introduced when relative measurements are linked to an absolute scale. Systematic errors are possible when data from two different labs are combined. All potential sources of error must be examined when estimating measurement error. CCGG measurement uncertainty is monitored using a variety of methods including 1) routine analysis of samples filled with air of known composition; 2) comparison of measurements from samples collected in pairs; 3) comparison of measurements made using independent methods; 4) frequent re-calibration of working reference gases; 5) regular calibration of the internal reference scale with an absolute scale; 6) ongoing inter-laboratory comparisons; and 7) periodic inter-laboratory comparisons. Each of these methods provides information useful in estimating uncertainty in CCGG measurements for use in inverse model studies.