A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO 2 , CO, CH 4 , N 2 O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remotesensing observations (better than 0.25% for CO 2 ). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

[1] When intercomparing measurements made by remote sounders, it is necessary to make due allowance for the differing characteristics of the observing systems, particularly their averaging kernels and error covariances. We develop the methods required to do this, applicable to any kind of retrieval method, not only to optimal estimators. We show how profiles and derived quantities such as the total column of a constituent may be properly compared, yielding different averaging kernels. We find that the effect of different averaging kernels can be reduced if the retrieval or the derived quantity of one instrument is simulated using the retrieval of the other. We also show how combinations of measured signals can be found, which can be compared directly. To illustrate these methods, we apply them to two real instruments, calculating the expected amplitudes and variabilities of the diagnostics for a comparison of CO measurements made by a ground-based Fourier Transform spectrometer (FTIR) and the ''measurement of pollution in the troposphere'' instrument (MOPITT), which is mounted on the EOS Terra platform. The main conclusions for this case are the following: (1) Direct comparison of retrieved profiles is not satisfactory, because the expected standard deviation of the difference is around half of the expected natural variability of the true atmospheric profiles. (2) Comparison of the MOPITT profile retrieval with a simulation using FTIR is much more useful, though still not ideal, with expected standard deviation of differences of around 20% of the expected natural variability. (3) Direct comparison of total columns gives an expected standard deviation of about 9%, while comparison of MOPITT with a simulation derived from FTIR improved this to 8%. (4) There is only one combination of measured signals that can be usefully compared. The difference is expected to have a standard deviation of about 5.5% of the expected natural variability, which is mostly due to noise.

Abstract. The Total Carbon Column Observing Network (TCCON) produces precise measurements of the column average dry-air mole fractions of CO 2 , CO, CH 4 , N 2 O and H 2 O at a variety of sites worldwide. These observations rely on spectroscopic parameters that are not known with sufficient accuracy to compute total columns that can be used in combination with in situ measurements. The TCCON must therefore be calibrated to World Meteorological Orga-

Abstract. This work describes the NASA Atmospheric CO 2 Observations from Space (ACOS) X CO 2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ∼0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

[1] Precision requirements are determined for space-based column-averaged CO 2 dry air mole fraction (X CO 2 ) data. These requirements result from an assessment of spatial and temporal gradients in X CO 2 , the relationship between X CO 2 precision and surface CO 2 flux uncertainties inferred from inversions of the X CO 2 data, and the effects of X CO 2 biases on the fidelity of CO 2 flux inversions. Observational system simulation experiments and synthesis inversion modeling demonstrate that the Orbiting Carbon Observatory mission design and sampling strategy provide the means to achieve these X CO 2 data precision requirements.

[1] The objective, design, and implementation of the OCO inverse method are presented. The inverse method is the algorithm which finds the profile-weighted mean mixing ratio, X CO2 , which best fits the measured spectrum, given a ''forward model'' which calculates the spectrum for a given atmospheric state, surface, and instrument properties. Minimizing bias among comparative values of X CO2 is a critical objective. The algorithm uses an ''optimal,'' maximum a posteriori inverse method, with weak a priori constraint, and employs a state vector containing atmospheric and surface properties expected to vary significantly between soundings. An extensive operational characterization and error analysis will be employed, producing quantities designed to aid atmospheric modelers in use of the OCO data. In particular, comparison to inverse models of surface CO 2 flux will require use of the OCO column averaging kernel and a priori state vector. An off-line error analysis has also been developed for more detailed error studies, and its use is illustrated by prospective application to case studies of nadir observations in summer and winter at three sites. Uncertainties due to noise, geophysical variability, and spectroscopic parameters are considered in detail. At low and midlatitudes, the single-sounding errors due to these sources are expected to be $0.7-0.8 ppm for high-sun conditions and $1.5-2.5 ppm for low sun (winter). Errors from the same sources in semimonthly regional averages are predicted to be <1 ppm for all conditions.

Abstract. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO 2 (X CO 2 ) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X CO 2 south of 25 • S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use Correspondence to: D. Wunch (dwunch@gps.caltech.edu) the observed correlation between free-tropospheric potential temperature and X CO 2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TC-CON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25 • S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

This work describes the NASA Atmospheric CO₂ Observations from Space (ACOS) X_CO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to imperfect meteorology, surface reflectance, and radiative transfer assumptions are small