Algorithm update of the GOSAT/TANSO-FTS thermal infrared CO<sub>2</sub> product (version 1) and validation of the UTLS CO<sub>2</sub> data using CONTRAIL measurements

Abstract

Abstract. The Thermal and Near Infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) has been observing carbon dioxide (CO 2 ) concentrations in several atmospheric layers in the thermal infrared (TIR) band since its launch. This study compared TANSO-FTS TIR version 1 (V1) CO 2 data and CO 2 data obtained in the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project in the upper troposphere and lower stratosphere (UTLS), where the TIR band of TANSO-FTS is most sensitive to CO 2 concentrations, to validate the quality of the TIR V1 UTLS CO 2 data from 287 to 162 hPa. We first evaluated the impact of considering TIR CO 2 averaging kernel functions on CO 2 concentrations using CO 2 profile data obtained by the CONTRAIL Continuous CO 2 Measuring Equipment (CME), and found that the impact at around the CME level flight altitudes (∼ 11 km) was on average less than 0.5 ppm at low latitudes and less than 1 ppm at middle and high latitudes. From a comparison made during flights between Tokyo and Sydney, the averages of the TIR upper-atmospheric CO 2 data were within 0.1 % of the averages of the CONTRAIL CME CO 2 data with and without TIR CO 2 averaging kernels for all seasons in the Southern Hemisphere. The results of comparisons for all of the eight airline routes showed that the agreements of TIR and CME CO 2 data were worse in spring and summer than in fall and winter in the Northern Hemisphere in the upper troposphere. While the differences between TIR and CME CO 2 data were on average within 1 ppm in fall and winter, TIR CO 2 data had a negative bias up to 2.4 ppm against CME CO 2 data with TIR CO 2 averaging kernels at the northern low and middle latitudes in spring and summer. The negative bias at the northern middle latitudes resulted in the maximum of TIR CO 2 concentrations being lower than that of CME CO 2 concentrations, which led to an underestimate of the amplitude of CO 2 seasonal variation.

Gradients of column CO<sub>2</sub> across North America from the NOAA Global Greenhouse Gas Reference Network

“…The uncertainty of TCCON total column CO 2 is reported to be 0.4 ppm (1σ ) after comparison to aircraft measurements (Wunch et al, 2010). Vertical profiles are also used to evaluate other satellite retrievals of total column XCO 2 , such as those from the Tropospheric Emission Spectrometer (TES) (Kulawik et al, 2013) and the Greenhouse Gases Observing SATellite (GOSAT) Saitoh et al, 2016). Satellite retrieval products have known and unknown biases (due to errors in spectroscopy, viewing geometry, spatial differences in clouds and aerosols, surface albedo, etc.)…”

A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion

“…However, even with the perfect (non-biased) transport model and the ideal prior error variances, improvements in the prior flux estimate in some regions have been found to be limited due to the sparseness of the observations. Further improvements are expected by adding data from in situ continuous measurements and worldwide observations by aircraft (e.g., Machida et al, 2008) and satellites (e.g., Yoshida et al, 2013;Saitoh et al, 2016) in a future study. Appendix C: Pseudo-observation sites …”

“…For example, in situ continuous observation measurements are now regularly taken at background stations, as well as at tall towers to infer regional continental fluxes (e.g., Sasakawa et al, 2010;Andrews et al, 2014). Moreover, worldwide aircraft observation programs have started to collect observations on a regular basis (e.g., Machida et al, 2008;Sawa et al, 2015;Matsueda et al, 2015), along with satellite observations dedicated to measurements of GHGs (Yoshida et al, 2013;Saitoh et al, 2016). These numerous GHG observational data can be exploited to estimate surface fluxes on a much finer scale than a subcontinental scale.…”

Satellite observation of atmospheric methane: intercomparison between AIRS and GOSAT TANSO-FTS retrievals

“…In the TANSO-FTS TIR V1.0 CH 4 retrieval processing, we simultaneously retrieve H 2 O, N 2 O, O 3 , and temperature other than CH 4 . We also simultaneously derive surface temperature and surface emissivity as a correction parameter of spectral bias inherent in TANSO-FTS TIR V161.160 L1B spectra in the same manner as CO 2 retrieval (Saitoh et al, 2016).The retrieval algorithm is a non-linear maximum a posteriori method with linear mapping (Rodgers, 2000). The a priori CH 4 profiles used in the retrieval are taken from the National Institute for Environmental Studies (NIES) transport model (Maksyutov et al, 2008;Saeki et al, 2013).…”