Validation of GOSAT / TANSO-FTS TIR UTLS CO 2 data ( Version 1 . 0 ) using CONTRAIL measurements

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Abstract
The thermal infrared (TIR) band of 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 since its launch.This study compared TANSO-FTS TIR V1.0 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 V1.0 UTLS CO 2 data from 287 to 162 hPa.From a comparison made during flights between Tokyo and Sydney, the averages of the TIR upper atmospheric CO 2 data agreed well with the averages of the data obtained by the CONTRAIL Continuous CO 2 Measuring Experiment (CME) within 0.1 % for all of the seasons in the Southern Hemisphere.The results of a comparison for all of the eight airline routes showed that the agreement between the TIR and CONTRAIL CO 2 data was within 0.5 % on average in the Northern Hemisphere, which was better than the agreement between a priori and CONTRAIL CO 2 data.The quality of TIR lower stratospheric CO 2 data depends largely on the information content, and therefore has a seasonal dependence.In high latitudes, TIR V1.0 lower stratospheric CO 2 data are only valid in the summer.The magnitude of bias in the TIR upper atmospheric CO 2 data did not have a clear longitudinal dependence.
The comparison results for flights in northern low and middle latitudes showed that the agreement between TIR and CONTRAIL CO 2 data in the upper troposphere was worse in the spring and summer than in the fall and winter.This could be attributed to a larger negative bias in the upper atmospheric a priori CO 2 data in the spring and summer and a seasonal dependence of spectral bias in TANSO-FTS TIR Level 1B (L1B) radiance data.The negative bias in northern middle latitudes made the maximum of TIR CO 2 concentrations lower than that of CONTRAIL CO 2 concentrations, which leads to underestimate the amplitude of CO 2 seasonal variation.

Introduction
Carbon dioxide (CO 2 ) in the atmosphere is a well-known strong greenhouse gas (IPCC, 2013, and references therein), with concentrations that have been observed both in situ and by satellite sensors.Its long-term observation began in Mauna Loa, Hawaii, and the South Pole in the late 1950s (Keeling et al., 1976a(Keeling et al., , b, 1996)).Since then, comprehensive CO 2 observations in the atmosphere have been conducted worldwide in several observatories and tall towers (Bakwin et al., 1998), by aircraft flask sampling (e.g., Crevoisier et al., 2010), and via the AirCore sampling system (Karion et al., 2010) in the framework of researches by the National Oceanic and Atmospheric Administration (NOAA).Atmospheric CO 2 concentrations have gradually increased at a globally averaged annual rate of 1.7 ± 0.5 ppm from 1998 to 2011, although its growth rate has relatively large interannual variation (IPCC, 2013).Upper atmospheric CO 2 observations have been made in many areas by several projects using commercial airliners, such as the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project (Machida et al., 2008) and the Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container (CARIBIC) project (Brenninkmeijer et al., 2007).Continuous long-term measurements of CO 2 made by several airplanes of Japan Airlines (JAL) in the CONTRAIL project have revealed details of its seasonal variation and interhemispheric transport in the upper atmosphere (Sawa et al., 2012) and interannual and long-term trends of its latitudinal gradients (Matsueda et al., 2015).
Atmospheric CO 2 observations by satellite sensors are categorized into two types: those utilizing CO 2 absorption bands in the shortwave infrared (SWIR) regions at around 1.6 and 2.0 µm, and those in the thermal infrared (TIR) regions at around 4.6, 10, and 15 µm.The Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) on the Environmental Satellite (ENVISAT) first observed CO 2 column-averaged dry-air mole fractions (X CO 2 ) from spectra at 1.57 µm (Buchwitz et al., 2005;Barkley et al., 2006).The Thermal and Near Infrared Sensor for Carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) on board the Green-Figures

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Full house Gases Observing Satellite (GOSAT), which was launched in 2009 (Yokota et al., 2009), has observed X CO 2 with high precision by utilizing the 1.6 and/or 2.0 µm CO 2 absorption bands (Yoshida et al., 2011(Yoshida et al., , 2013;;O'Dell et al., 2012;Butz et al., 2011;Cogan et al., 2012).The Orbiting Carbon Observatory 2 (OCO-2) was successfully launched in 2014, and started regular observations of X CO 2 with high spatial resolution.Satellite CO 2 observations at TIR absorption bands have a longer history beginning with the High-Resolution Infrared Sounder (HIRS) (Chédin et al., 2002(Chédin et al., , 2003(Chédin et al., , 2005)).The Atmospheric Infrared Sounder (AIRS) has achieved more accurate observations of middle and upper tropospheric CO 2 concentrations (Crevoisier et al., 2004;Chahine et al., 2005;Maddy et al., 2008;Strow and Hannon, 2008).The Tropospheric Emission Spectrometer (TES) has retrieved CO 2 concentrations in several vertical layers with high accuracy by taking advantage of its high wavelength resolution (Kulawik et al., 2010(Kulawik et al., , 2013)).The Infrared Atmospheric Sounding Interferometer (IASI) has derived upper atmospheric CO 2 amounts from its TIR spectra (Crevoisier et al., 2009).TANSO-FTS also has a TIR band in addition to its three SWIR bands, and obtains vertical information of CO 2 concentrations in addition to X CO 2 in the same field of view (Saitoh et al., 2009).Rayner and O'Brien (2001) and Pak and Prather (2001) showed the utility of global CO 2 data obtained by satellite sensors for estimating its source and sink strength, and many studies of CO 2 inversion have been conducted using a huge amount of satellite data since the 2000s.Chevallier et al. (2005) first used satellite CO 2 data, observed with the Operational Vertical Sounder (TOVS), to estimate CO 2 surface fluxes.They reported that a regional bias in satellite CO 2 data hampers the outcomes.Nassar et al. (2011) demonstrated that the wide spatial coverage of satellite CO 2 data is beneficial to CO 2 surface flux inversion through the combined use of TES and surface flask CO 2 data, particularly in regions where surface measurements are sparse.In addition to CO 2 surface inversion results using TIR observations, global X CO 2 data observed with the SWIR bands of TANSO-FTS have been actively used for estimating CO 2 source and sink strength (Maksyutov et al., 2013;Saeki et al., 2013a;Cheval-Figures Back Close Full  , 2014;Basu et al., 2013Basu et al., , 2014;;Takagi et al., 2014).One of the important things to consider when incorporating satellite data in CO 2 inversion is the accuracy of the data, as suggested by Basu et al. (2013).Uncertainties in satellite CO 2 data should be assessed seasonally and regionally to determine the seasonal and regional characteristics of the satellite CO 2 bias.
The importance of upper atmospheric CO 2 data in the inversion analysis of CO 2 surface fluxes was discussed in Niwa et al. (2012).They used CONTRAIL CO 2 data in conjunction with surface CO 2 data to estimate surface flux, and demonstrated that adding middle and upper tropospheric data observed by the aircraft could greatly reduce the posteriori flux errors, particularly in tropical Asian regions.Middle and upper tropospheric and lower stratospheric CO 2 concentrations and column amounts of CO 2 can be simultaneously observed in the same field of view with TANSO-FTS on board GOSAT.Provided that the quality of upper atmospheric CO 2 data simultaneously obtained with TANSO-FTS is proven to be comparable to that of TANSO-FTS X CO 2 data (Yoshida et al., 2013;Inoue et al., 2013), the combined use of upper atmospheric CO 2 and X CO 2 data observed with TANSO-FTS could be a useful tool for estimating CO 2 surface flux.
GOSAT, which is the first satellite to be dedicated to greenhouse gas monitoring, was launched on 23 January 2009.As described above, the TIR band of TANSO-FTS on board GOSAT has been observing CO 2 concentrations in several vertical layers.
In this study, we focused on CO 2 concentrations in the upper troposphere and lower stratosphere (UTLS), where the TIR band of TANSO-FTS is most sensitive.We validated these data by comparison with upper atmospheric CO 2 data obtained in a wide spatial coverage in the CONTRAIL project.Sections 2 and 3 explain the GOSAT and CONTRAIL measurements, respectively.Section 4 details the retrieval algorithm used

GOSAT observations
GOSAT is a joint satellite project of the National Institute for Environmental Studies (NIES), Ministry of the Environment (MOE), and Japan Aerospace Exploration Agency (JAXA) for the purpose of making global observations of greenhouse gases such as CO 2 and CH 4 (Hamazaki et al., 2005;Yokota et al., 2009).It was launched on 23 January 2009, from the Tanegashima Space Center, and has continued its observations for more than six years.GOSAT is equipped with the TANSO-FTS for greenhouse gas monitoring and the TANSO-Cloud and Aerosol Imager (CAI) to detect clouds and aerosols in the TANSO-FTS field of view (Kuze et al., 2009).TANSO-FTS consists of three bands in the SWIR region and one band in the TIR region.The SWIR bands observe column amounts of greenhouse gases and the TIR band observes vertical information of gas concentrations (Yoshida et al., 2011(Yoshida et al., , 2013;;Saitoh et al., 2009Saitoh et al., , 2012;;Ohyama et al., 2012Ohyama et al., , 2013)).Kuze et al. (2012) provided a detailed description of the methods used for the processing and calibration of level 1B (L1B) spectral data from TANSO-FTS.They explained the algorithm for the version 150.151 (V150.151)L1B spectral data.The TIR V1.0 L2 CO 2 product we focused on in this study was created from a later version, V161.160, of L1B spectral data.The following modifications were made to the algorithm from V150.151 to V161.160: improving the TIR radiometric calibration through the improvement of calibration parameters, turning off the sampling interval non-uniformity correction, modifying the spike noise criteria of the quality flag, and reevaluating the misalignment between the GOSAT satellite and TANSO-FTS sensor.Kataoka et al. (2014) reported that the accuracies of TANSO-FTS TIR V130.130L1B radiance spectra based on comparisons with the Scanning High-resolution Interferometer Sounder (S-HIS) spectra for warm scenes were 0.5 K at 800-900 and 700- bias inherent in TIR L1B spectra would be mainly because of uncertainty of polarization correction.Another possible cause was discussed in Imasu et al. (2010).When retrieving CO 2 concentrations from the TIR band of TANSO-FTS, the spectral bias that is predominant in CO 2 absorption bands should be considered (Ohyama et al., 2013).

CONTRAIL Continuous Measurement Equipment (CME) observations
We used CO 2 data obtained in the CONTRAIL project to validate the quality of TANSO-FTS TIR V1.0 L2 CO 2 data.CONTRAIL is a project to observe atmospheric trace gases such as CO 2 and CH 4 using instruments installed on commercial aircraft operated by JAL.Observations of trace gases in this project began in 2005.Two types of measurement instruments, the Automatic Air Sampling Equipment (ASE) and the Continuous CO 2 Measuring Equipment (CME), have been installed on several JAL aircraft to measure trace gases over a wide area (Machida et al., 2008).This study used CO 2 data obtained with CME on several airline routes from Narita Airport, Japan.CO 2 observations with CME use a LI-COR LI-840 instrument that utilizes a nondispersive infrared absorption (NDIR) method (Machida et al., 2008).In the observations, two different standard gases, with CO 2 concentration of 340 and 390 ppm based on NIES09 scale, are regularly introduced into the NDIR for calibration.The accuracy of CME CO 2 measurements is 0.2 ppm.See Machida et al. (2008), Matsueda et al. (2008), andMachida et al. (2011) for details of the CME CO 2 observations and their accuracy and precision.Figures band of TANSO-FTS was basically processed by the algorithm described in Saitoh et al. (2009).The V1.0 L2 CO 2 product that we focused on in this study also adopted a non-linear maximum a posteriori (MAP) method with linear mapping, as was the case for the V00.01 product.We utilized the following expressions in TIR CO 2 retrieval: where x a is an a priori vector, S a is a covariance matrix of the a priori vector, S ε is a covariance matrix of measurement noise, K i is a CO 2 Jacobian matrix calculated using the i th retrieval vector xi on full grids, F( xi ) is a forward spectrum vector based on xi , y is a measurement spectrum vector, and ẑi+1 is the i + 1th retrieval vector defined on retrieval grids.W is a matrix that interpolates from retrieval grids onto full grids.W * is the generalized inverse matrix of W.
The full grids are vertical layer grids for radiative transfer calculation, and the retrieval grids are defined as a subset of the full grids.In the V1.0 L2 CO 2 retrieval algorithm, linear mapping between retrieval grids and full grids was also applied, but the number of full grid levels was 78 instead of 110 in the V00.01 algorithm.The determination of retrieval grids in the V1.0 algorithm basically followed the method of the V00.01 algorithm.It was based on the areas of a CO 2 averaging kernel matrix in the tropics, but the retrieval grid levels were fixed for all of the retrieval processing, as presented in Table 1.Averaging kernel matrix A is defined (Rodgers, 2000) as Figure 1 shows typical averaging kernel functions of TIR V1.0 L2 CO 2 retrieval in middle latitudes in summer.The degree of freedom (DF) in this case (trace of the matrix A) was 2.09.The seasonally averaged DF values of TIR V1.0 CO 2 data ranged from 1.12 to 2.35.In the middle latitude between 35 A priori and initial values for CO 2 concentrations were taken from the outputs of the NIES transport model (NIES-TM05) (Saeki et al., 2013b).A priori and initial values for temperature and water vapor were obtained from Japan Meteorological Agency (JMA) Grid Point Value (GPV) data.Basically, the retrieval processing of TANSO-FTS was only conducted under clear-sky conditions, which was judged based on a cloud flag from TANSO-CAI in the daytime (Ishida and Nakajima, 2009;Ishida et al., 2011) and on a TANSO-FTS TIR spectrum in the nighttime.

Improvements of TIR V1.0 CO 2 algorithm
The following conditions are the improvements made in the TANSO-FTS TIR V1.0 L2 CO 2 algorithm from the V00.01 algorithm.The V1.0 algorithm used the CO 2 10 µm absorption band in addition to the CO 2 absorption band at around 15 µm band; the wavelength regions of 690-750, 790-795, 930-990, and 1040-1090 cm −1 were used in the CO 2 retrieval.In these wavelength regions, temperature, water vapor, and ozone concentrations were retrieved simultaneously with CO 2 concentration.Moreover, surface temperature and surface emissivity were simultaneously derived as a correction parameter of the spectral bias inherent in TANSO-FTS TIR V161.160L1B spectra at the above-mentioned CO 2 absorption bands.We assumed that the spectral bias could be divided into two components: a wavelength-dependent bias whose amount varied depending on wavelength and a wavelength-independent bias whose amount was uniform in a certain wavelength region.We tried to correct such a wavelength-independent component of the spectral bias by adjusting the value of surface temperature.K and S a of expression Eq. ( 1) are as follows: where fixed diagonal elements with a standard deviation of 2.5 %, 3 K, 20, and 30 %, respectively.Here, a priori and initial values for ozone were obtained from the climatological data for each latitude bin for each month given by MacPeters et al. (2007).We assumed rather large values as a priori variances of the surface parameters (a standard deviation of 10 K for surface temperature), which could allow more flexibility in the L1B spectral bias correction by the surface parameters.The a priori and initial values for surface emissivity were calculated on the basis of land-cover classification, vegetation, and wind speed.The a priori and initial values for surface temperature were estimated using radiance data in several channels around 900 cm −1 of the TIR V161.160L1B spectra.

Effects of spectral bias on CO 2 retrieval
In the TIR V1.0 L2 algorithm, we estimated surface temperature and surface emissivity to correct the spectral bias inherent in the TANSO-FTS TIR L1B spectra (Kataoka et al., 2014).Here, we evaluated the impact of the correction of the TIR L1B spectral bias through the simultaneous retrieval of the surface parameters on the TIR L2 CO 2 retrieval.Figure 2 shows comparisons between several types of TIR CO 2 profiles retrieved by changing the treatment of the surface parameters in the retrieval and coincident CONTRAIL CME CO 2 profiles over Narita airport.Criteria for the coincident pairs of a 100 km distance from Narita airport, a time difference in 2 h, and a day difference within ±1 day yielded a total of 141 coincident profile pairs in 2010.In the comparisons, we applied averaging kernel functions of TIR CO 2 data to corresponding CONTRAIL CME CO 2 profiles, as follows (Rodgers and Connor, 2003): (5) Here, x CONTRAIL and x a priori are CONTRAIL CME and a priori CO 2 profiles.Figure 2a shows a comparison of the V1.0 L2 CO 2 product (i.e., the result of a comparison of CO 2 retrievals based on TANSO-FTS TIR L1B spectra corrected through the simultaneous retrieval of both surface temperature and surface emissivity).Figure 2b and c shows 13003 Introduction

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Full the results of a comparison of CO 2 retrievals that used TIR L1B spectra corrected only by surface temperature and surface emissivity, respectively.Figure 2d shows the result of a comparison of CO 2 retrievals from uncorrected original TIR L1B spectra.The existence of a relatively large spectral bias around the CO 2 15 µm absorption band in TANSO-FTS TIR L1B spectra (Kataoka et al., 2014) resulted in a decrease in the number of normally retrieved CO 2 profiles.In the V1.0 case (Fig. 2a), CO 2 profiles were normally retrieved for 74 of the 114 coincident pairs.The comparison between Fig. 2a and c (Fig. 2b and d) demonstrated that the correction of the TIR L1B spectral bias through the simultaneous retrieval of surface temperature could increase the number of normally retrieved CO 2 profiles (in this case, from 48 to 74).This implies that a wavelength-independent component of the spectral bias in CO 2 absorption bands could be reduced by adjusting the value of surface temperature at the bands.In contrast, the comparison between Fig. 2a and b (Fig. 2c and d) showed that the correction of the TIR L1B spectral bias through the simultaneous retrieval of surface emissivity had a relatively small impact on TIR L2 CO 2 retrieval.Nevertheless, surface emissivity, which has a wavelength dependence, can be effective for correcting the wavelengthdependent L1B spectral bias.A more effective method of L1B spectral bias correction based on surface emissivity should be considered in the next version of the TIR L2 CO 2 retrieval algorithm, if a future version of the TIR L1B spectral data still has a bias.Tokyo-Moscow (NRT-DME), Tokyo-Vancouver (NRT-VYR), Tokyo-Honolulu (NRT-HNL), Tokyo-Bangkok (NRT-BKK), Tokyo-Singapore (NRT-SIN) and Tokyo-Jakarta (NRT-CGK), and Tokyo-Sydney (NRT-SYD).We merged the level flight data of Tokyo-Amsterdam and Tokyo-Moscow into "Tokyo-Europe", and the data of Tokyo-Singapore and Tokyo-Jakarta into "Tokyo-East Asia".Figure 3  and Tokyo-Honolulu, the averages and standard deviations of TIR V1.0 CO 2 data and the corresponding a priori CO 2 data were calculated separately for the tropospheric and stratospheric data.In this calculation, we first selected TIR V1.0 CO 2 data that were collected in a range within ±1 km of the average altitudes of the CONTRAIL tropospheric and stratospheric CO 2 data for each area.Then, we classified each of the selected TIR CO 2 data points into tropospheric and stratospheric data on the basis of the temperature lapse rates from the JMA GPV data that were interpolated to the TANSO-FTS measurement locations, and calculated the seasonal averages and standard deviations for the reselected tropospheric and stratospheric TIR CO 2 data.This procedure was required for two reasons.One was that a tropopause height at each TANSO-FTS measurement location should differ on a daily basis.The other was that because TANSO-FTS TIR CO 2 data were selected within the range of 2 km, some tropospheric TIR CO 2 data were selected on the basis of the CONTRAIL stratospheric level flight data, and vice versa.Figure 4 shows the number of TANSO-FTS TIR CO 2 data points that were finally selected in each retrieval layer for each of the airline routes.
The TIR CO 2 data used in the comparative analysis were from layer 9 and layer 10 (from 287 to 196 hPa) for the tropospheric comparison and from layer 10 and layer 11 (from 237 to 162 hPa) for the stratospheric comparison.
We did not apply TIR CO 2 averaging kernels to CONTRAIL CME CO 2 data in the following UTLS analysis.Because CO 2 concentrations below and above the CONTRAIL CME flight levels were not observed except over airports, assuming a CO 2 vertical profile for each of CONTRAIL CME level flight data points and applying averaging kernels to the assumed CONTRAIL CO 2 profiles would increase the uncertainty in the CONTRAIL CO 2 data.Here, we assess the effect of not applying averaging kernels to CONTRAIL CME level flight data.Figure 5a shows the means of the averaging kernels of each of the three layers 9, 10, and 11 of all of the TANSO-FTS TIR CO 2 profiles used in the comparisons in summer.In we had data for comparison both in the troposphere and stratosphere.Considering the half-value width of the averaging kernels in Fig. 5a, the TANSO-FTS TIR CO 2 retrieval results in layers 9-11 would be affected by CO 2 concentrations from ∼ 400 to ∼ 120-130 hPa.As shown in the CONTRAIL CME ascending/descending CO 2 profiles in Fig. 5b, the variability in the CO 2 concentration from ∼ 400 to ∼ 200 hPa was relatively small in summer; the same was true in the other three seasons.This indicates that CO 2 concentrations below layer 9 had a small impact on the TIR CO 2 retrieval results in layers 9 and 10, which suggests that the following results do not change much, even when considering the averaging kernels related to the layers below layer 9. Consequently, we determined not to apply TIR CO 2 averaging kernels to CONTRAIL CME CO 2 data in this study.However, because we did not have CONTRAIL CME CO 2 data above ∼ 200 hPa, we could not evaluate the impact of the CO 2 concentration above ∼ 200 hPa on TANSO-FTS TIR CO 2 retrieval results in layers 9-11 on the basis of observation data.Thus, we should discuss again the effect of not applying averaging kernels to CONTRAIL CME data on the following comparison results in Sect.6.

Results of the comparisons
The airline route between Tokyo and Sydney covered a wide latitude range from the northern mid-latitudes (35 • N) to southern mid-latitudes (34 • S). Figure 6 shows the comparisons among CONTRAIL CME level flight, TANSO-FTS TIR, and a priori CO 2 data during flights between Tokyo and Sydney in spring.The average of the TIR CO 2 data agreed better to the average of the CONTRAIL CO 2 data than the a priori CO 2 data in all of the latitudes.In the Southern Hemisphere, the average of the TIR CO 2 data was within 0.1 % of the average of the CONTRAIL CO 2 data.In the Northern Hemisphere, the average of the TIR CO 2 data agreed with the average of the CON-TRAIL CO 2 data to within 0.5 %, although their agreement became slightly worse there compared to the Southern Hemisphere.
Along the airline route between Tokyo and Europe, both tropospheric and stratospheric CO 2 data were obtained in the CONTRAIL CME observations.Therefore, we 13007 Introduction

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Full were able to validate the quality of TANSO-FTS TIR CO 2 data for this route both in the upper troposphere and lower stratosphere using the UTLS CONTRAIL CO 2 data.Figure 7b and c shows that the differences between the upper tropospheric and lower stratospheric CO 2 concentrations of CONTRAIL CME data were approximately 2-3 ppm in the winter (maximum of 4.24 ppm in area 14).The upper tropospheric and lower stratospheric CO 2 concentrations from TANSO-FTS TIR V1.0 data also differed clearly, while the upper tropospheric and lower stratospheric CO 2 concentrations from a priori data were similar.The upper tropospheric CO 2 concentrations were in fairly good agreement with the corresponding CONTRAIL CME data (Fig. 7b).In the lower stratosphere in winter (Fig. 7c), the averages of the CONTRAIL CME level flight, TANSO-FTS TIR, and a priori CO 2 data were nearly identical.
Figure 8 shows the results of all of the comparisons among CONTRAIL CME, TANSO-FTS TIR, and a priori CO 2 data for each of the six (eight) airline routes for each season.The thick and dashed lines indicate the differences between CONTRAIL CME and TANSO-FTS TIR CO 2 data (TIR ave.minus CONTRAIL ave.) and the differences between CONTRAIL CME and a priori CO 2 data (a priori ave.minus CONTRAIL ave.) for each of the areas along the airline routes.All of the results with more than three data points are presented.Overall, the thick lines are closer to zero than the dashed lines, which means that TIR CO 2 data agreed better to CONTRAIL CO 2 data than a priori CO 2 data.
For the airline route between Tokyo and Europe (Fig. 8a), the agreement between tropospheric TANSO-FTS TIR and CONTRAIL CME CO 2 average data seemed slightly better in winter, although comparisons among seasons in the troposphere were difficult because of the lack of CONTRAIL CME data in high latitudes in the spring and summer (Fig. 8a1).In the stratosphere (Fig. 8a2), the averages of TIR and CONTRAIL CO 2 data agreed well with each other, and their differences were within ∼ 0.5-1 ppm in the spring, summer, and winter.The differences between the two averages were slightly larger in the fall (approximately 2 ppm).For the airline route between Tokyo and Vancouver (Fig. 8b), the averages of the TIR CO 2 data were more similar to the averages of Introduction

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Full the CONTRAIL CO 2 data than the a priori CO 2 data both in the upper troposphere and lower stratosphere in the fall and winter; the differences between the TIR and CONTRAIL CO 2 average data were approximately within 1 ppm.For the airline route between Tokyo and Honolulu (Fig. 8c), the agreement between TIR and CONTRAIL CO 2 average data did not show clear seasonal differences in the lower stratosphere (Fig. 8c2), because of a small number of stratospheric data.In contrast, in the upper troposphere (Fig. 8c1), the differences between the two were clearly larger in the spring and summer than in the fall and winter.In particular, both the differences between TIR and CONTRAIL CO 2 data and between a priori and CONTRAIL CO 2 data were larger in spring, as was the case for the results of the comparison for the airline route between Tokyo and Vancouver (Fig. 8b1).
Then, we focused on the results of the comparison between TANSO-FTS TIR and CONTRAIL CME upper tropospheric CO 2 data obtained in northern low and middle latitudes.Figure 8d shows that the agreement between TIR and CONTRAIL CO 2 average data was worse in the spring and summer than in the fall and winter for the airline route between Tokyo and Bangkok.The differences between TIR and CONTRAIL CO 2 data exceeded the 1-σ standard deviations of the averages of TIR CO 2 data, and were larger than the differences between a priori and CONTRAIL CO 2 data at 23-34 • N (area 20) in the summer.Similarly, the agreement between the averages of TIR and CONTRAIL CO 2 data was worse in the spring and summer than in the fall and winter for the airline route between Tokyo and East Asia (Fig. 8e).
For the airline route between Tokyo and Sydney (Fig. 8f), the average of the TANSO-FTS TIR CO 2 data was within 1 ppm of the average of the CONTRAIL CME CO 2 data in the Southern Hemisphere in all of the seasons, as in the comparison in the spring shown in Fig. 6.However, in the Northern Hemisphere, the agreement between the two was not as strong in all of the seasons.In the comparisons in the northern summer, although the differences between the average TIR and CONTRAIL CO 2 data were less than 1 % (3 ppm), there was a relatively large negative bias in the TIR CO 2 data against the CONTRAIL CO 2 data compared to the other seasons.In the upper troposphere in Introduction

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Full northern summer, TIR CO 2 data showed a significantly good agreement with CON-TRAIL CO 2 data compared to a priori CO 2 data in the Southern Hemisphere.However, in northern low and middle latitudes, TIR and a priori CO 2 data had a negative bias of up to 1 % against CONTRAIL CO 2 data.

Discussion
Figure 9 shows the averages of the partial degree of freedom of TANSO-FTS TIR V1.0 L2 CO 2 data for each of the areas along the airline route between Tokyo and Europe in the upper troposphere (a) and the lower stratosphere (b) for each season.The partial DF is defined as the trace of a submatrix of averaging kernels corresponding to a partial column of TIR CO 2 data that were compared to CONTRAIL CME level flight data, which is equal to the averages of the 9th, 10th, or 11th diagonal element of matrix A. As shown in Fig. 9, the values of the partial DF of TIR lower stratospheric CO 2 data were clearly lower than those of TIR upper tropospheric CO 2 data for all of the fights between Tokyo and Europe.TIR upper tropospheric CO 2 data were from layers 9 and 10, and TIR lower stratospheric CO 2 data were from layers 10 and 11, as shown in Fig. 4, which led to a clear difference in partial DF values between the TIR upper tropospheric and lower stratospheric CO 2 data.The partial DF values of TIR upper tropospheric CO 2 data were 0.13-0.20 in all of the areas for all seasons.In contrast, the partial DF values of TIR lower stratospheric CO 2 data in the spring, fall, and winter were ∼ 0.05 in almost all of the areas, although they were as high as 0.1-0.14 in the summer.The thick lines in Fig. 8a2 show that the agreement of TIR and CONTRAIL CO 2 data in the lower stratosphere was better in the spring, summer, and winter than in the fall.The dashed lines in Fig. 8a2 also show that a priori CO 2 data agreed better with CONTRAIL CME CO 2 data in the lower stratosphere in the spring and winter than in the summer and fall.From the results shown in Fig. 8a2 and Fig. 9, we conclude that TIR CO 2 retrieval results in the lower stratosphere in the spring and winter were constrained to the relatively good a priori CO 2 data due to the low information content, Introduction

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Full and consequently had a good agreement with CONTRAIL CO 2 data.In the fall, TIR CO 2 retrieval results in the lower stratosphere were constrained to the relatively poor a priori CO 2 data, and therefore had a negative bias of approximately 2 ppm against CONTRAIL CO 2 data.In the summer, TIR CO 2 retrievals had a relatively high information content compared to the other seasons, which led to a good agreement between TIR and CONTRAIL CO 2 data despite the relatively poor a priori CO 2 data.In conclusion, the quality of TIR V1.0 CO 2 data in the lower stratosphere depends largely on the information content compared to the upper troposphere.In the case of high latitude measurements, TIR V1.0 lower stratospheric CO 2 data are only valid in the summer.
As shown in Fig. 8d-f, the agreement between TANSO-FTS TIR and CONTRAIL CME CO 2 data was worse in the spring and summer than in the fall and winter in northern low and middle latitudes.At these latitudes, TIR upper tropospheric CO 2 data had a negative bias of up to ∼ 1 % against CONTRAIL upper tropospheric CO 2 data.This characteristic was also seen in the data from flights between Tokyo and Honolulu where there was a large amount of upper tropospheric data available in all of the seasons (Fig. 8c1).Overall, as shown in Fig. 8, a priori CO 2 data used in TIR V1.0 CO 2 retrievals had a negative bias against CONTRAIL CO 2 data.The a priori CO 2 data in the spring and summer in Fig. 8d-f clearly had a larger negative bias against the corresponding CONTRAIL CO 2 data.The results show that a priori CO 2 data taken from NIES-TM 05 underestimate the increase in the CO 2 concentration in the upper atmosphere in spring and summer, which results in a larger negative bias of TIR V1.0 upper tropospheric CO 2 data in the spring and summer than in the fall and winter in northern low and middle latitudes.
In general, information content of CO 2 observations made by TIR sensors is higher in middle and high latitudes in the spring and summer than in the fall and winter because of thermal contrast in the atmosphere, with less seasonal dependence in low latitudes.Therefore, in the spring and summer, retrieved CO 2 data contain more measurement information and are less constrained by a priori data in all latitudes.However, as shown in Fig. 8, the retrieved TIR CO 2 data in the Northern Hemisphere did not sufficiently Introduction

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Full reduce the negative bias of the a priori CO 2 data in the spring and summer.The degree of improvement in the spring and summer was comparable to or worse than in the fall and winter.This implies the existence of factors that worsened CO 2 retrieval results other than the poor a priori data in the spring and summer.One of the possible factors is uncertainty in JMA GPV temperature profiles used in TIR V1.0 L2 CO 2 retrieval.If they have some seasonal bias, seasonally dependent bias in retrieved CO 2 data would be produced.However, the TIR V1.0 algorithm simultaneously retrieved temperature profiles other than CO 2 , and therefore the effect of temperature uncertainty on retrieved CO 2 data should be reduced.
Another possible factor that worsened CO 2 retrieval results is uncertainty in the calibration of TIR V161.160L1B spectra.This means that the amount of TIR V161.160L1B spectral bias has some seasonal dependence.Therefore, we investigated an appropriate parameter to evaluate the uncertainty in TANSO-FTS TIR L1B spectra.The temperatures of the internal blackbody on board the TANSO-FTS instrument partly reflect the environmental thermal condition inside the instrument.The temperatures of FTS-mechanics and aft-optics on the optical bench of the TANSO-FTS instrument are precisely controlled at 23 • C. The difference in temperature between the environment inside the instrument and the optical bench would cause the uncertainty in radiometric calibration of TANSO-FTS L1B spectra.Thus, the temperatures of the internal blackbody on board the TANSO-FTS instrument could be a parameter to evaluate the TANSO-FTS TIR L1B spectral bias.
Figure 10 is a scatter-plot of the average temperatures of the onboard internal blackbody and the average differences between TANSO-FTS TIR and CONTRAIL CME CO 2 data shown in Fig. 8 for each area for each season.The average temperatures of the on-board internal blackbody were lower in the spring and summer than in the fall and winter in all of the areas.It can be seen from Fig. 10 that the internal blackbody temperatures in the summer (diamonds) were lower than those in the other seasons (crosses).As discussed above, a priori CO 2 data had a larger negative bias against CONTRAIL CME CO 2 data particularly in northern low and middle latitudes in the spring and sum-Introduction

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Full mer, which led to a larger negative bias in retrieved TIR CO 2 data at these latitudes.In addition, retrieved TIR CO 2 data had a larger bias in summer when the internal blackbody temperatures were lower, even if the amount of negative bias in a priori CO 2 data in summer was comparable to that in the other seasons, as shown in Fig. 10.As stated above, the temperature of the onboard internal blackbody could be a candidate for evaluating the spectral bias.At this moment, however, there is no definite evidence of a clear correlation between the temperatures of the onboard internal blackbody and the bias in TANSO-FTS V161.160L1B spectra that would subsequently cause the seasonally dependent bias in the TIR V1.0 L2 CO 2 data, because the correlation between the average temperatures of the onboard internal blackbody and the average differences between TIR and CONTRAIL CO 2 data is not very strong.
The TANSO-FTS TIR V1.0 L2 CO 2 algorithm simultaneously retrieves surface temperature and surface emissivity as a corrective parameter of the bias in TIR L1B spectra.Therefore, the uncertainty in these surface parameters would have a large impact on retrieved TIR CO 2 profiles.Figure 7d and e in Saitoh et al. (2009) show that the uncertainty of retrieved UTLS CO 2 concentrations in layers 9-11 is much less than 1 % when the surface parameters have 1 % uncertainty, although the uncertainty of CO 2 concentrations at ∼ 400 hPa reaches 3 % for the same condition.We conclude that the uncertainty of the surface parameters has a relatively small impact on the TIR UTLS CO 2 concentrations that were the focus of this study.The uncertainties in surface parameters and water vapor in lower atmosphere largely affect lower and middle tropospheric TIR CO 2 data, and therefore should be discussed when validating the quality of TIR CO 2 data in the lower and middle troposphere, which is beyond the scope of this paper.
We compared TANSO-FTS TIR V1.0 L2 upper tropospheric and lower stratospheric CO 2 data that were mainly from layers 9 and 10 and from layers 10 and 11 with the corresponding CONTRAIL CME tropospheric and stratospheric CO 2 data without applying the TIR CO 2 averaging kernels to the CONTRAIL CO 2 data.As discussed above, CO 2 concentrations below layer 9 have a small impact on TIR CO 2 retrieval results in layers Figures

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Full 9 and 10, because the variability in the CO 2 concentration from ∼ 400 to ∼ 200 hPa was relatively small in all of the seasons.However, in layer 11, TIR CO 2 retrieval results could be overestimated by the effect of the CO 2 concentration below layer 9, if the atmosphere in layer 11 is stratospheric air with relatively low CO 2 concentrations.On the other hand, TIR CO 2 retrieval results in layers 9-11 could also be affected by CO 2 concentration from ∼ 200 to ∼ 120-130 hPa, judging from the half-value width of the averaging kernels in Fig. 5a.If the atmosphere from ∼ 200 to ∼ 120-130 hPa is stratospheric air with low CO 2 concentrations, retrieved TIR CO 2 concentrations in layers 9-11 could be underestimated.In summary, retrieved TIR CO 2 concentrations could be underestimated in layers 9-10, and face the conflicting possibility of being overestimated and/or underestimated in layer 11.However, because we did not have CO 2 observation data below and above the CONTRAIL CME flight levels, we cannot reach a definite conclusion.As shown in Fig. 8, TIR upper tropospheric CO 2 data had a slightly negative bias against CONTRAIL CME CO 2 data.In the comparison of the airline route of Tokyo-Sydney as shown in Fig. 6, the differences between the averages of TIR CO 2 data and the averages of CONTAIL CO 2 data were slightly larger in the Northern Hemisphere (0.5 %) than in the Southern Hemisphere (0.1 %).The difference between upper tropospheric and lower stratospheric CO 2 concentrations is larger in the Northern Hemisphere in spring (Sawa et al., 2012), which would cause a slightly larger negative bias in the Northern Hemisphere than in the Southern Hemisphere.
The effect of lower CO 2 concentrations from ∼ 200 to ∼ 120-130 hPa on TIR CO 2 retrieval results in layers 9-10 could be one of the causes of a negative bias in retrieved CO 2 data other than the negative bias of a priori CO 2 data and the spectral bias of TIR V161.160L1B spectra.We investigated differences between TIR and CONTRAIL CO 2 comparison results in layers 9-11 with and without applying averaging kernel functions although in limited areas over several airports where CO 2 vertical profiles were observed.Nonhydrostatic Icosahedral Atmospheric Model (NICAM)-Transport Model (TM) (Niwa et al., 2011) to create CO 2 profiles over airports.In northern middle latitudes in the spring (over NRT airport), considering averaging kernel functions by using expression Eq. ( 5) decreased a negative bias in TIR CO 2 data in layers 9 and 10 by ∼ 1 ppm.On the other hand, the same tendency was not seen in southern middle latitudes in the spring (SYD) when considering averaging kernel functions.This is consistent with the above discussion related to Sawa et al. ( 2012).In the summer and fall in middle latitudes in both hemispheres, the effect of considering averaging kernel functions on TIR and CONTRAIL CO 2 comparison results was negligible (less than ∼ 0.5 ppm), although CONTRAIL CO 2 data in layers 9 and 10 with averaging kernel functions became slightly larger there.In low latitudes (BKK, SIN, and CGK), differences between TIR and CONTRAIL CO 2 comparison results in layers 9 and 10 with and without considering averaging kernel functions were also negligible in every season.In northern high latitudes (AMS and YVR), bias of TIR lower stratospheric CO 2 data against CON-TRAIL CO 2 data in layers 10 and 11 tended to diminish when considering averaging kernel functions, and the effect of considering averaging kernel functions on TIR and CONTRAIL upper tropospheric CO 2 comparison results in layers 9 and 10 was again negligible.
Using CONTRAIL CME level flight observations that covered wide spatial areas makes us discuss a longitudinal difference in the characteristics of TIR UTLS CO 2 data.
In the comparison results of the airline routes of Tokyo-Europe and Tokyo-Vancouver shown in Fig. 8a and b, the magnitudes of differences between TIR and CONTRAIL CO 2 data were similar in every longitude in the fall and winter in the upper troposphere and in every season in the lower stratosphere, although there is little logic to discuss the longitudinal differences in the spring and summer in the upper troposphere because of a small number of the data.On the other hand, in the comparison results of Tokyo-Honolulu, differences between TIR and CONTRAIL CO 2 data became larger toward ∼ 165 • W (195 • in Fig. 8c1) in the spring.This area is located at 25 • N, and differences between TIR and CONTRAIL CO 2 data were also large in area 20 (in the airline route Figures of Tokyo-Bangkok), area 27 (Tokyo-East Asia), and area 28 (Tokyo-Sydney) located in the same latitude region, which implies that these biases depended on latitude, not on longitude.We conclude that the data quality of TIR V1.0 L2 UTLS CO 2 data does not have a clear longitudinal dependence.Finally, we evaluated bias values of TIR V1.0 CO 2 data against CONTRAIL CME CO 2 data for each season for each of the latitude regions: 60-70 • N (areas 3-13), 40-60 • N (areas 1, 2, 14, 35, 37, 41-43), 20-40 • N (areas 15, 20, 21, 27, 28, 36, 38-40), 0-20 • N (areas 18, 22, 19, 25, 26, 29,  30), 0-20 data by ∼ 1 ppm in the spring and slightly increased a negative bias in the summer in northern middle latitudes.Thus, the following negative biases should be considered when incorporating TIR V1.0 upper tropospheric CO 2 data in inverse models which usually consider averaging kernel functions: ∼ 2.0 ppm in both spring and summer in 20-40 • N, ∼ 1.0 ppm in spring and ∼ 1.5 ppm in summer in 40-60 • N. In northern low latitudes (0-20 • N), the negative bias of 2.0 ppm should be taken into account in summer, as presented in Table 2.In the lower stratosphere in northern high latitudes, bias of TIR CO 2 data against CONTRAIL CME CO 2 data tended to diminish when considering averaging kernel functions.It is the negative biases in northern low and middle latitudes that we should mainly care about when using TIR V1.0 L2 CO 2 data in any scientific analysis.In the upper troposphere in northern middle latitudes, CO 2 concentrations reach the maximum from spring through early summer.The negative biases in TIR CO 2 data there make the maximum of TIR CO 2 concentrations lower than that of CONTRAIL CME CO 2 concentrations, which leads to underestimate the amplitude of Introduction

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Full CO 2 seasonal variation when using TIR CO 2 data without taking their negative biases into account.

Summary
In this study, we conducted a comprehensive validation of the UTLS CO 2 concentrations from the GOSAT/TANSO-FTS TIR V1.0 L2 CO 2 product.The TIR V1.0 L2 CO 2 algorithm used both the CO 2 10 and 15 µm absorption bands (690-750, 790-795, 930-990, and 1040-1090 cm −1 ), and simultaneously retrieved vertical profiles of CO 2 , water vapor, ozone, and temperature in these wavelength regions.Because TANSO-FTS TIR V161.160L1B radiance data used in the TIR V1.0 L2 CO 2 retrieval had a spectral bias, we simultaneously derived surface temperature and surface emissivity in the same wavelength regions just as a corrective parameter, other than temperature and gas profiles, to correct the spectral bias.The simultaneous retrieval of surface temperature greatly increased the number of normally retrieved CO 2 profiles.
To validate the quality of TIR V1.0 upper atmospheric CO 2 data, we compared them with the level flight CO 2 data of the CONTRAIL CME observations along the following airline routes in 2010: Tokyo-Europe (Amsterdam and Moscow), Tokyo-Vancouver, Tokyo-Honolulu, Tokyo-Bangkok, Tokyo-East Asia (Singapore and Jakarta), and Tokyo-Sydney.For the CONTRAIL data obtained during the northern high latitude flights, we made comparisons among CONTRAIL, TIR, and a priori CO 2 data separately in the upper troposphere and in the lower stratosphere.The TIR upper tropospheric and lower stratospheric CO 2 data that were compared were mainly from layers 9 and 10 (287-196 hPa) and from layers 10 and 11 (237-162 hPa), respectively.
In the Southern Hemisphere, the averages of TIR upper atmospheric CO 2 data were within 0.1 % of the averages of CONTRAIL CO 2 data for all of the seasons, from the limited comparisons made during flights between Tokyo and Sydney.In the Northern Hemisphere, TIR CO 2 data had a better agreement with CONTRAIL CO 2 data than a priori CO 2 data, with the agreement being on average within 0.5 %.The northern high Introduction

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Full latitude comparisons suggest that the quality of TIR lower stratospheric CO 2 data depends largely on the information content.In high latitudes, TIR V1.0 lower stratospheric CO 2 data are only valid in the summer when their information content is highest.In the northern low and middle latitudes, the agreement between TIR and CONTRAIL CO 2 data in the upper troposphere was worse in the spring and summer than that in the fall and winter, partly because of a larger negative bias in the a priori CO 2 data in the spring and summer than in the fall and winter.In addition, a seasonal dependence of the spectral bias inherent to TANSO-FTS TIR L1B radiance data could cause a negative bias in retrieved CO 2 concentrations, particularly in summer.TIR sensors can make more observations than SWIR sensors.The combined use of TIR UTLS CO 2 data and X CO 2 data from the SWIR bands of TANSO-FTS can be useful for studies of CO 2 surface flux inversion and atmospheric transport, provided that the seasonally and regionally dependent negative biases of the TIR V1.0 L2 CO 2 data presented here are taken into account.Introduction

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Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | lier et al.
in the latest version 1.0 (V1.0)CO 2 level 2 (L2) product of the TIR band of TANSO-FTS.Sections 5 and 6 show and discuss the results of a comparison between TANSO-FTS TIR V1.0 L2 and CONTRAIL CO 2 data.Section 7 summarizes this study.Discussion Paper | Discussion Paper | Discussion Paper | 750 cm−1 , 0.1 K at 980-1080 cm −1 , and more than 2 K at 650-700 cm −1 .Although the magnitude of the spectral bias evaluated on the basis of V130.130L1B data would change in V161.160L1B data, the issue of L1B spectral bias still remains.The spectral Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Similarly, a wavelength-dependent component of the spectral bias was corrected by adjusting the value of surface emissivity in each wavelength channel.Therefore the matrices of Discussion Paper | Discussion Paper | Discussion Paper | and K T are Jacobian matrices of CO 2 , water vapor, ozone, and temperature on full grids, respectively, and S CO 2 , S H 2 O , S O 3 , and S T are a priori covariance matrices of CO 2 , water vapor, ozone, and temperature on full grids, respectively.The vectors k sT_1 , k sT_2 , k sT_3 , k sT_4 , and k sT_5 are the Jacobian vectors of surface temperature in the wavelength regions of 690-715, 715-750, 790-795, 930-990, and 1040-1090 cm −1 , respectively.The vectors k sE_1 , k sE_2 , k sE_3 , k sE_4 , and k sE_5 are the Jacobian vectors of surface emissivity in each of the five wavelength regions, respectively.The elements of the Jacobian vectors of surface parameters that were defined for each of the five wavelength regions were set to be zero in the other wavelength regions.The values S sT_1 , S sT_2 , S sT_3 , S sT_4 , and S sT_5 and S sE_1 , S sE_2 , S sE_3 , S sE_4 , and S sE_5 are a priori variances of surface temperature and surface emissivity in each of the five wavelength regions, respectively.Simultaneous retrieval of the surface parameters in the V1.0 algorithm was conducted just for the purpose of correcting the TIR V161.160L1B spectral bias; it had no physical meaning.We estimated the surface parameters separately in each of the five wavelength regions to consider differences in the amount of spectral bias in each wavelength region.The matrices S a for CO 2 , temperature, water vapor, and ozone were diagonal matrices with vertically Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

5
Comparisons of TANSO-FTS TIR V1.0 upper atmospheric CO 2 data with CME CO 2 data 5.1 Area comparisons Here, we used the level flight CO 2 data of CONTRAIL CME observations in 2010 to validate the quality of UTLS CO 2 data from the TANSO-FTS TIR V1.0 L2 CO 2 product.The level flight data obtained in the following eight airline routes of the CON-TRAIL CME observations were used in this study: Tokyo-Amsterdam (NRT-AMS) and Introduction Discussion Paper | Discussion Paper | Discussion Paper | shows the flight tracks of all of the CONTRAIL CME observations in 2010 used in this study.As shown in the figure, we divided the CONTRAIL CME level flight data into 40 areas following Niwa et al. (2012), and compared them with TANSO-FTS TIR CO 2 data in each area.The level flight data in each area were averaged for each season (MAM, JJA, SON, and JF/DJF).The amount of level flight data varied depending on the area and season.The largest amount was obtained in area 15 over Narita Airport, where 4694-9306 data points were obtained.A relatively small amount of level flight data, 79-222 data points, was obtained in area 1 over Amsterdam.In all 40 areas, we collected an enough amount of level flight data to undertake a comparative analysis based on the average values, except for seasons and regions with no flights.The average altitude of all of the CONTRAIL CME level flight data used here was 11.245 km.The airline routes of Tokyo-Europe, Tokyo-Vancouver, and Tokyo-Honolulu contained both tropospheric and stratospheric data in the areas along their routes; therefore, we calculated the average and standard deviation values separately.Here, we differentiated between the tropospheric and stratospheric level flight data on the basis of temperature lapse rates from the JMA GPV data that were interpolated to the CONTRAIL CME measurement locations.The average altitudes of the tropospheric and stratospheric level flight data from the airline route between Tokyo and Europe were 10.84 and 11.18 km, respectively.Next, we selected TANSO-FTS TIR V1.0 L2 CO 2 data that were in the altitude range within ±1 km of the average altitude of the CONTRAIL level flight data for each area for each season, and calculated their averages and standard deviations.Similarly, we calculated the averages and standard deviations of the corresponding a priori CO 2 data for each area for each season.For the airline routes of Tokyo-Europe, Tokyo-Vancouver, Discussion Paper | Discussion Paper | Discussion Paper | Fig. 5, we show examples of area 40 in the airline route between Tokyo and Honolulu, where we had a large amount of data for comparison, and area 1 in the airline route between Tokyo and Amsterdam, where Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Following the method proposed by Araki et al. (2010), we used CONTRAIL ascending/descending CO 2 data below a tropopause and stratospheric CO 2 concentrations taken from the Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | spectral observations by the Greenhouse gases observing satellite, Atmos.Meas.Tech., 4, 717-734, doi:10.5194/amt-4-717-2011,2011.Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A., Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P. O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |
• S (areas23, 31, 32), and 20-40• S (areas 33, 34).The bias values are the weighted averages of differences between TIR and CONTRAIL averaged CO 2 data of the areas located in each latitude region with considering the number of TIR CO 2 data in each of the areas.In the upper troposphere in 20-60 • N, negative biases in TIR CO 2 data ranging from 2.2 to 2.7 ppm and from 1.2 to 1.6 ppm were seen in the spring and summer, respectively, as summarized in Table2.Although the evaluation on the basis of NICAM-TM stratospheric CO 2 concentrations in limited areas over several airports, considering averaging kernel functions decreased a negative bias in TIR CO 2

Table 2 .
Bias values of GOSAT/TANSO-FTS TIR V1.0 CO 2 data against CONTRAIL CME CO 2 data for each season and each latitude region in the upper troposphere and lower stratosphere in the unit of ppm.Significant bias values larger than ±1.0 ppm are indicated by boldface.The evaluation of the bias values does not consider TIR CO 2 averaging kernel functions.