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NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, X$_{CO_{2}}$, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X$_{CO_{2}}$ from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 X$_{CO_{2}}$ retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X$_{CO_{2}}$ data quality throughout its mission.

Understanding the atmospheric distribution of water (H$_{2}$O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH$_{2}$O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH$_{2}$O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of -3.1%± 17.7%, reasonable consistency over different locations (station bias of -3.1%±9.5%) and very good correlation with TCCON (R = 0.95). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH$_{2}$O amounts can reach 6000–6500ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH$_{2}$O product, after public release, can already be useful for water cycle studies.

Model simulations of column-averaged methane mixing ratios (XCH4) are extensively used for inverse estimates of methane (CH4) emissions from atmospheric measurements. Our study shows that virtually all chemical transport models (CTM) used for this purpose are affected by stratospheric model-transport errors. We quantify the impact of such model transport errors on the simulation of stratospheric CH4 concentrations via an a posteriori correction method. This approach compares measurements of the mean age of air with modeled age and expresses the difference in terms of a correction to modeled stratospheric CH4 mixing ratios. We find age differences up to ~ 3 years yield to a bias in simulated CH4 of up to 250 parts per billion (ppb). Comparisons between model simulations and ground-based XCH4 observations from the Total Carbon Column Network (TCCON) reveal that stratospheric model-transport errors cause biases in XCH4 of ~ 20 ppb in the midlatitudes and ~ 27 ppb in the arctic region. Improved overall as well as seasonal model-observation agreement in XCH4 suggests that the proposed, age-of-air-based stratospheric correction is reasonable. The latitudinal model bias in XCH4 is supposed to reduce the accuracy of inverse estimates using satellite-derived XCH4 data. Therefore, we provide an estimate of the impact of stratospheric model-transport errors in terms of CH4 flux errors. Using a one-box approximation, we show that average model errors in stratospheric transport correspond to an overestimation of CH4 emissions by ~ 40 % (~ 7 Tg yr−1) for the arctic, ~ 5 % (~ 7 Tg yr−1) for the northern, and ~ 60 % (~ 7 Tg yr−1) for the southern hemispheric mid-latitude region. We conclude that an improved modeling of stratospheric transport is highly desirable for the joint use with atmospheric XCH4 observations in atmospheric inversions.

The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (−8.85 and 4.75 ppm for XCO2 and −20.4 and 18.9 ppb for XCH4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (−1.48 and 2.09 ppm for XCO2 and −5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas.

Within the NDACC (Network for the Detection of Atmospheric Composition Change), more than 20 FTIR (Fourier-transform infrared) spectrometers, spread worldwide, provide long-term data records of many atmospheric trace gases. We present a method that uses measured and modelled XCO2 for assessing the consistency of these NDACC data records. Our XCO2 retrieval setup is kept simple so that it can easily be adopted for any NDACC/FTIR-like measurement made since the late 1950s. By a comparison to coincident TCCON (Total Carbon Column Observing Network) measurements, we empirically demonstrate the useful quality of this suggested NDACC XCO2 product (empirically obtained scatter between TCCON and NDACC is about 4‰ for daily mean as well as monthly mean comparisons, and the bias is 25‰). Our XCO2 model is a simple regression model fitted to CarbonTracker results and the Mauna Loa CO2 in situ records. A comparison to TCCON data suggests an uncertainty of the model for monthly mean data of below 3‰. We apply the method to the NDACC/FTIR spectra that are used within the project MUSICA (multi-platform remote sensing of isotopologues for investigating the cycle of atmospheric water) and demonstrate that there is a good consistency for these globally representative set of spectra measured since 1996: the scatter between the modelled and measured XCO2 on a yearly time scale is only 3‰.