Abstract
Abstract. Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability.
Highlights
Our ability to infer surface carbon fluxes depends critically on interpreting spatial and temporal variations of atmospheric CO2 and relating them back to surface fluxes
In order to determine if the retrieval approach, forward model, and understanding of uncertainties are robust, it is crucial to determine if the calculated uncertainties are consistent with the actual uncertainties
The bias and its uncertainties in Total Carbon Column Observing Network (TCCON) column-averaged CO2 are explained at two different time scales: 4-h time windows centered about individual aircraft measurement and day-to-day time scales from comparison to the collection of 41 aircraft profiles
Summary
Our ability to infer surface carbon fluxes depends critically on interpreting spatial and temporal variations of atmospheric CO2 and relating them back to surface fluxes. In addition to the column CO2 from NIR measurements, free tropospheric CO2 measurements can be made from passive thermal infrared satellite instruments such as Tropospheric Emission Spectrometer (TES); (Kulawik et al, 2010, 2012) and AIRS (Chahine et al, 2005) All these measurements by different techniques play important roles in the carbon flux inversion problem and provide complementary information of the atmospheric CO2 distribution. We present a method to estimate the LT CO2 by combining column and FT CO2 from two data sources: total column estimates from TCCON and free tropospheric estimates from TES data, assimilated into the GEOS-Chem model We expect this approach to provide estimates of lower tropospheric CO2, because the TCCON and TES measurements have complementary sensitivities to the vertical distribution of CO2. As long as the retrievals converge and the estimated states are close to the true states, the problem of subtracting free tropospheric column amount from total column amount is a linear problem with well-characterized uncertainties
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