齋藤 尚子

Abstract The Greenhouse gases Observing SATellite 2 (GOSAT-2) was launched in January 2018 as a successor to GOSAT (launched in 2009), the first satellite to specialize in greenhouse gas observations. Compared to the GOSAT sensors, the sensors of GOSAT-2 offer higher performance in most respects. The quality and quantity of data from observations are expected to be improved accordingly. The signal-to-noise ratio (SNR) is better in both the SWIR and TIR bands of TANSO-FTS-2, which is the main sensor of GOSAT-2. This improvement ultimately enhances the accuracy of greenhouse gas concentration analysis. Furthermore, because of the improved SNR in the SWIR band, the northern limit at which data are obtainable in high-latitude regions of the Northern Hemisphere in winter, where observation data have remained unavailable because of weak signal strength, has moved to higher latitudes. As better data are obtained in greater quantities, progress in carbon cycle research for high-latitude regions is anticipated. Moreover, the improvement of SNR in the TIR band is expected to be considerable. Particularly, the resolutions of the vertical concentration distributions of CO₂ and CH₄ have been improved drastically. The first function introduced for GOSAT-2 that is not in GOSAT is an intelligent pointing mechanism: a cloud area avoidance function using the in-field camera of TANSO-FTS-2. This function can increase the amounts of observation data globally and can improve the accuracy of CO₂ emissions estimation and measurements of uptake intensity. The effects are expected to be strong, especially for the tropics because cumulus clouds are the most common cloud type. The intelligent pointing system can avoid the clouds effectively. Another important benefit of TANSO-FTS-2 is that the wavelength range of Band 3 of SWIR has been expanded for measuring carbon monoxide (CO). Because CO originates from combustion, it is used to evaluate some effects of human activities in urban areas and biomass burning in fields. Particularly, black carbon-type aerosols can be measured by the sub-sensor, TANSO-CAI-2, to assess biomass burning along with CO₂ and CO by TANSO-FTS-2.

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

Methane (CH4) is an important greenhouse gas and plays a significant role in tropospheric and stratospheric chemistry. Despite the relevance of methane (CH4) in human-induced climate change and air pollution chemistry, there is no scientific consensus on the causes of changes in its growth rates and variability over the past three decades. We use a well-validated chemistry-transport model for simulating CH4 concentration and estimation of regional CH4 emissions by inverse modeling during 1988-2016. The control simulations are conducted using seasonally varying hydroxyl (OH) concentrations and assumed no interannual variability. Using inverse modeling of atmospheric observations, emission inventories, a wetland model, and a delta C-13-CH4 box model, we show that reductions in emissions from Europe and Russia since 1988, particularly from oil-gas exploitation and enteric fermentation, led to decreased CH4 growth rates in the 1990s. This period was followed by a quasi-stationary state of CH4 in the atmosphere during the early 2000s. CH4 resumed growth from 2007, which we attribute to increases in emissions from coal mining mainly in China and the intensification of ruminant farming in tropical regions. A sensitivity simulation using interannually varying OH shows that regional emission estimates by inversion are unaffected for the mid- and high latitude areas. We show that meridional shift in CH4 emissions toward the lower latitudes and the increase in CH4 loss by hydroxyl (OH) over the tropics finely balance out, keeping the CH4 gradients between the southern hemispheric tropical and polar sites relatively unchanged during 1988-2016. The latitudinal emissions shift is confirmed using the global distributions of the total column CH4 observations via satellite remote sensing. During our analysis period, there is no evidence of emission enhancement due to climate warming, including the boreal regions. These findings highlight key sectors for effective emission reduction strategies toward climate change mitigation.

Aerosol optical properties are measured near the surface level using sampling instruments and a near-horizontal lidar. The values of the aerosol extinction coefficient inside the instruments are derived from nephelometer and aethalometer data, while the ambient values are measured from the lidar. The information on aerosol size distribution from optical particle counters is used to simulate extinction coefficients using the Mie scattering theory, with corrections on the humidity growth of hygroscopic particles. By applying this method to the continuous data obtained from November to December 2018 at Chiba, Japan, we elucidate the temporal variations of near-surface aerosol properties, including the complex refractive index, single scattering albedo, and Angstrom exponent. The results indicate how aerosol particles change their properties between the dry, instrumental conditions and relatively humid setting of the ambient atmosphere. (C) 2020 Optical Society of America

Hyperspectral thermal infrared sounders enable us to grasp the global behavior of minor atmospheric constituents. Ammonia, which imparts large impacts on the atmospheric environment by reacting with other species, is one of them. In this work, we present an ammonia retrieval system that we developed for the Greenhouse Gases Observing Satellite (GOSAT) and the estimates of global atmospheric ammonia column amounts that we derived from 2009 to 2014. The horizontal distributions of the seasonal ammonia column amounts represent significantly high values stemming from six anthropogenic emission source areas and four biomass burning ones. The monthly mean time series of these sites were investigated, and their seasonality was clearly revealed. A comparison with the Infrared Atmospheric Sounding Interferometer (IASI) ammonia product showed good agreement spatially and seasonally, though there are some differences in detail. The values from GOSAT tend to be slightly larger than those from IASI for low concentrations, especially in spring and summer. On the other hand, they are lower for particularly high concentrations during summer, such as eastern China and northern India. In addition, the largest differences were observed in central Africa. These differences seem to stem from the temporal gaps in observations and the fundamental differences in the retrieval systems.

We performed a feasibility study of constraining the vertical profile of the tropospheric ozone by using a synergetic retrieval method on multiple spectra, i.e., ultraviolet (UV), thermal infrared (TIR), and microwave (MW) ranges, measured from space. This work provides, for the first time, a quantitative evaluation of the retrieval sensitivity of the tropospheric ozone by adding the MW measurement to the UV and TIR measurements. Two observation points in East Asia (one in an urban area and one in an ocean area) and two observation times (one during summer and one during winter) were assumed. Geometry of line of sight was nadir down-looking for the UV and TIR measurements, and limb sounding for the MW measurement. The retrieval sensitivities of the ozone profiles in the upper troposphere (UT), middle troposphere (MT), and lowermost troposphere (LMT) were estimated using the degree of freedom for signal (DFS), the pressure of maximum sensitivity, reduction rate of error from the a priori error, and the averaging kernel matrix, derived based on the optimal estimation method. The measurement noise levels were assumed to be the same as those for currently available instruments. The weighting functions for the UV, TIR, and MW ranges were calculated using the SCIATRAN radiative transfer model, the Line-By-Line Radiative Transfer Model (LBLRTM), and the Advanced Model for Atmospheric Terahertz Radiation Analysis and Simulation (AMATERASU), respectively. The DFS value was increased by approximately 96, 23, and 30% by adding the MW measurements to the combination of UV and TIR measurements in the UT, MT, and LMT regions, respectively. The MW measurement increased the DFS value of the LMT ozone; nevertheless, the MW measurement alone has no sensitivity to the LMT ozone. The pressure of maximum sensitivity value for the LMT ozone was also increased by adding the MW measurement. These findings indicate that better information on LMT ozone can be obtained by adding constraints on the UT and MT ozone from the MW measurement. The results of this study are applicable to the upcoming air-quality monitoring missions, APOLLO, GMAP-Asia, and uvSCOPE.

Abstract. CO2 observations in the free troposphere can be useful for constraining CO2 source and sink estimates at the surface since they represent CO2 concentrations away from point source emissions. 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 global CO2 concentrations in the free troposphere for about 8 years and thus could provide a dataset with which to evaluate the vertical transport of CO2 from the surface to the upper atmosphere. This study evaluated biases in the TIR version 1 (V1) CO2 product in the lower troposphere (LT) and the middle troposphere (MT) (736–287 hPa), on the basis of comparisons with CO2 profiles obtained over airports using Continuous CO2 Measuring Equipment (CME) in the Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) project. Bias-correction values are presented for TIR CO2 data for each pressure layer in the LT and MT regions during each season and in each latitude band: 40–20° S, 20° S–20° N, 20–40° N, and 40–60° N. TIR V1 CO2 data had consistent negative biases of 1–1.5 % compared with CME CO2 data in the LT and MT regions, with the largest negative biases at 541–398 hPa, partly due to the use of 10 µm CO2 absorption band in conjunction with 15 and 9 µm absorption bands in the V1 retrieval algorithm. Global comparisons between TIR CO2 data to which the bias-correction values were applied and CO2 data simulated by a transport model based on the Nonhydrostatic ICosahedral Atmospheric Model (NICAM-TM) confirmed the validity of the bias-correction values evaluated over airports in limited areas. In low latitudes in the upper MT region (398–287 hPa), however, TIR CO2 data in northern summer were overcorrected by these bias-correction values; this is because the bias-correction values were determined using comparisons mainly over airports in Southeast Asia, where CO2 concentrations in the upper atmosphere display relatively large variations due to strong updrafts.

Abstract. The primary instrument on the Greenhouse gases Observing SATellite (GOSAT) is the Thermal And Near infrared Sensor for carbon Observations (TANSO) Fourier transform spectrometer (FTS). TANSO-FTS uses three short-wave infrared (SWIR) bands to retrieve total columns of CO2 and CH4 along its optical line of sight and one thermal infrared (TIR) channel to retrieve vertical profiles of CO2 and CH4 volume mixing ratios (VMRs) in the troposphere. We examine version 1 of the TANSO-FTS TIR CH4 product by comparing co-located CH4 VMR vertical profiles from two other remote-sensing FTS systems: the Canadian Space Agency's Atmospheric Chemistry Experiment FTS (ACE-FTS) on SCISAT (version 3.5) and the European Space Agency's Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat (ESA ML2PP version 6 and IMK-IAA reduced-resolution version V5R_CH4_224/225), as well as 16 ground stations with the Network for the Detection of Atmospheric Composition Change (NDACC). This work follows an initial inter-comparison study over the Arctic, which incorporated a ground-based FTS at the Polar Environment Atmospheric Research Laboratory (PEARL) at Eureka, Canada, and focuses on tropospheric and lower-stratospheric measurements made at middle and tropical latitudes between 2009 and 2013 (mid-2012 for MIPAS). For comparison, vertical profiles from all instruments are interpolated onto a common pressure grid, and smoothing is applied to ACE-FTS, MIPAS, and NDACC vertical profiles. Smoothing is needed to account for differences between the vertical resolution of each instrument and differences in the dependence on a priori profiles. The smoothing operators use the TANSO-FTS a priori and averaging kernels in all cases. We present zonally averaged mean CH4 differences between each instrument and TANSO-FTS with and without smoothing, and we examine their information content, their sensitive altitude range, their correlation, their a priori dependence, and the variability within each data set. Partial columns are calculated from the VMR vertical profiles, and their correlations are examined. We find that the TANSO-FTS vertical profiles agree with the ACE-FTS and both MIPAS retrievals' vertical profiles within 4 % (± ∼  40 ppbv) below 15 km when smoothing is applied to the profiles from instruments with finer vertical resolution but that the relative differences can increase to on the order of 25 % when no smoothing is applied. Computed partial columns are tightly correlated for each pair of data sets. We investigate whether the difference between TANSO-FTS and other CH4 VMR data products varies with latitude. Our study reveals a small dependence of around 0.1 % per 10 degrees latitude, with smaller differences over the tropics and greater differences towards the poles.

Space-borne observations of atmospheric methane (CH4) have been made using the Atmospheric Infrared Sounder (AIRS) on the EOS/Aqua satellite since August 2002 and the Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSOFTS) on the Greenhouse Gases Observing Satellite (GOSAT) since April 2009. This study compared the GOSAT TANSOFTS thermal infrared (TIR) version 1.0 CH4 product with the collocated AIRS version 6 CH4 product using data from 1 August 2010 to 30 June 2012, including the CH4 mixing ratios and the total column amounts. The results show that at 300-600 hPa, where both AIRS and GOSAT-TIR CH4 have peak sensitivities, they agree very well, but GOSAT-TIR retrievals tend to be higher than AIRS in layer 200-300 hPa. At 300 hPa the CH4 mixing ratio from GOSAT-TIR is, on average, 10.3 +/- 31.8 ppbv higher than that from AIRS, and at 600 hPa GOSAT-TIR retrieved CH4 is 16.2 +/- 25.7 ppbv lower than AIRS CH4. Comparison of the total column amount of CH4 shows that GOSAT-TIR agrees with AIRS to within 1% in the mid-latitude regions of the Southern Hemisphere and in the tropics. In the mid to high latitudes in the Northern Hemisphere, comparison shows that GOSAT-TIR is similar to 1-2% lower than AIRS, and in the high-latitude regions of the Southern Hemisphere the difference of GOSAT from AIRS varies from 3% in October to +2% in July. The difference between AIRS and GOSAT TANSO-FTS retrievals is mainly due to the difference in retrieval algorithms and instruments themselves, and the larger difference in the highlatitude regions is associated with the low information content and small degrees of freedom of the retrieval. The degrees of freedom of GOSAT-TIR retrievals are lower than that of AIRS, which also indicates that the constraint in GOSAT-TIR retrievals may be too strong. From the good correlation between AIRS and GOSAT-TIR retrievals and the seasonal variation they observed, we are confident that the thermal infrared measurements from AIRS and GOSAT-TIR can provide valuable information to capture the spatial and temporal variation of CH4, especially in the mid-upper troposphere, in most periods and regions.

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 (CO2) concentrations in several atmospheric layers in the thermal infrared (TIR) band since its launch. This study compared TANSO-FTS TIR version 1 (V1) CO2 data and CO2 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 CO2 concentrations, to validate the quality of the TIR V1 UTLS CO2 data from 287 to 162 hPa. We first evaluated the impact of considering TIR CO2 averaging kernel functions on CO2 concentrations using CO2 profile data obtained by the CONTRAIL Continuous CO2 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 CO2 data were within 0.1 % of the averages of the CONTRAIL CME CO2 data with and without TIR CO2 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 CO2 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 CO2 data were on average within 1 ppm in fall and winter, TIR CO2 data had a negative bias up to 2.4 ppm against CME CO2 data with TIR CO2 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 CO2 concentrations being lower than that of CME CO2 concentrations, which led to an underestimate of the amplitude of CO2 seasonal variation.

Abstract. We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three data sets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier transform spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier transform infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Laboratory at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional collocation criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1 DOFS per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS–PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ±3 × 1022 molecules cm−2. Expressed as median ± median absolute deviation (expressed in partial column units or as a percentage), these differences are 0.11 ± 9.60  × 1020 molecules cm−2 (0.012 ± 1.018 %) for TANSO-FTS–PEARL-FTS, −2.6 ± 2.6 × 1021  cm−2 (−1.6 ± 1.6 %) for ACE-FTS–PEARL-FTS, and 7.4 ± 6.0 × 1020 molecules cm−2 (0.78 ± 0.64 %) for TANSO-FTS–ACE-FTS. The differences for ACE-FTS–PEARL-FTS and TANSO-FTS–PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS–ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns.

Abstract. An algorithm based on CO2 slicing, which has been used for cirrus cloud detection using thermal infrared data, was developed for high-resolution radiance spectra from satellites. The channels were reconstructed based on sensitivity height information of the original spectral channels to reduce the effects of measurement errors. Selection of the reconstructed channel pairs was optimized for several atmospheric profile patterns using simultaneous studies assuming a cloudy sky. That algorithm was applied to data by the Greenhouse gases Observing SATellite (GOSAT). Results were compared with those obtained from the space-borne lidar instrument on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Monthly mean cloud amounts from the slicing generally agreed with those from CALIPSO observations despite some differences caused by surface temperature biases, optically very thin cirrus, multilayer structures of clouds, extremely low cloud tops, and specific atmospheric conditions. Comparison of coincident data showed good agreement, except for some cases, and revealed that the improved slicing method is more accurate than the traditional slicing method. Results also imply that improved slicing can detect low-level clouds with cloud top heights as low as approximately 1.5 km.

Abstract. The space and time variabilities of methane (CH4) total column and upper tropospheric mixing ratios are analysed above the Mediterranean Basin (MB) as part of the Chemical and Aerosol Mediterranean Experiment (ChArMEx) programme. Since the analysis of the mid-to-upper tropospheric CH4 distribution from spaceborne sensors and model outputs is challenging, we have adopted a climatological approach and have used a wide variety of data sets. We have combined spaceborne measurements from the Thermal And Near infrared Sensor for carbon Observations – Fourier Transform Spectrometer (TANSO-FTS) instrument on the Greenhouse gases Observing SATellite (GOSAT) satellite, the Atmospheric InfraRed Spectrometer (AIRS) on the AURA platform and the Infrared Atmospheric Sounder Interferometer (IASI) instrument aboard the MetOp-A platform with model results from the Chemical Transport Model (CTM) MOCAGE, and the Chemical Climate Models (CCMs) CNRM-AOCCM and LMDz-OR-INCA (according to different emission scenarios). In order to minimize systematic errors in the spaceborne measurements, we have only considered maritime pixels over the MB. The period of interest spans from 2008 to 2011 considering satellite and MOCAGE data and, regarding the CCMs, from 2001 to 2010. Although CH4 is a long-lived tracer with lifetime of ~12 years and is supposed to be well mixed in the troposphere, an east–west gradient in CH4 is observed and modelled in the mid-to-upper troposphere with a maximum in the Western MB in all seasons except in summer when CH4 accumulates above the Eastern MB. The peak-to-peak amplitude of the east–west seasonal variation in CH4 above the MB in the upper troposphere (300 hPa) is weak but almost twice as great in the satellite measurements (~25 ppbv) as in the model data (~15 ppbv). The maximum of CH4 in summer above the eastern MB can be explained by a series of dynamical processes only occurring in summer. The Asian monsoon traps and uplifts high amounts of CH4 to the upper troposphere where they build up. The Asian Monsoon Anticyclone redistributes these elevated CH4 amounts towards North Africa and the Middle East to finally reach and descend in the eastern MB. In the lower troposphere, the CH4 variability is mainly driven by the local sources of emission in the vicinity of the MB.

The distribution of atmospheric carbon dioxide (CO2) in the subarctic was investigated using the National Institute for Environmental Studies (NIBS) three-dimensional transport model (TM) and retrievals from the Greenhouse gases Observing SATellite (GOSAT). Column-averaged dry air mole fractions of subarctic atmospheric CO2 (XCO2) from the NIES TM for four flux combinations were analyzed. Two flux datasets were optimized using only surface observations and two others were optimized using both surface and GOSAT Level 2 data. Two inverse modeling approaches using GOSAT data were compared. In the basic approach adopted in the GOSAT Level 4 product, the GOSAT observations are aggregated into monthly means over 5 degrees x 5 degrees grids. In the alternative method, the model observation misfit is estimated for each observation separately. The XCO2 values simulated with optimized fluxes were validated against Total Carbon Column Observing Network (TCCON) ground-based high-resolution Fourier Transform Spectrometer (FTS) measurements. Optimized fluxes were applied to study XCO2 seasonal variability over the period 2009-2010 in the Arctic and subarctic regions. The impact on CO2 levels of emissions from enhancement of biospheric respiration induced by the high temperature and strong wildfires occurring in the summer of 2010 was analyzed. Use of GOSAT data has a substantial impact on estimates of the level of CO2 interanual variability. (C) 2014 Elsevier B.V. and NIPR. All rights reserved.

We present surface CO2 flux estimates obtained by an inverse modeling analysis from column-averaged dry air mole fractions of CO2 (XCO2) observed by the Greenhouse gases Observing SATellite (GOSAT) and ground-based data. Two inversion cases were examined: 1) a decadal inversion using ground-based CO2 observations by NOAA from 1999 to 2010 to derive CO2 flux interannual variability, and 2) an inversion using NOAA plus NIES GOSAT XCO2 data from June 2009 to October 2010. We used single-shot GOSAT data and individual NOAA flask data for the inversions. Our results show differences in estimated fluxes between the NOAA data inversion and the NOAA plus GOSAT data inversion, especially in Northern Eurasia and in Equatorial Africa and America where the ground-based observational sites were sparse. Uncertainty reduction rates of 40%-70% were achieved by inclusion of GOSAT data, compared to the case using just the NOAA data. The inclusion of GOSAT data in the inversion resulted in larger summer sinks in northwest Boreal Eurasia and a smaller summer sink in southeast Boreal Eurasia, with a clear uncertainty reduction in both regions. Adding GOSAT data also led to increase in Tropical African fluxes in boreal winter beyond interannual variability from NOAA data inversions.

We present the application of a global carbon cycle modeling system to the estimation of monthly regional CO2 fluxes from the column-averaged mole fractions of CO2 (XCO2) retrieved from spectral observations made by the Greenhouse gases Observing SATellite (GOSAT). The regional flux estimates are to be publicly disseminated as the GOSAT Level 4 data product. The forward modeling components of the system include an atmospheric tracer transport model, an anthropogenic emissions inventory, a terrestrial biosphere exchange model, and an oceanic flux model. The atmospheric tracer transport was simulated using isentropic coordinates in the stratosphere and was tuned to reproduce the age of air. We used a fossil fuel emission inventory based on large point source data and observations of nighttime lights. The terrestrial biospheric model was optimized by fitting model parameters to observed atmospheric CO2 seasonal cycle, net primary production data, and a biomass distribution map. The oceanic surface pCO2 distribution was estimated with a 4-D variational data assimilation system based on reanalyzed ocean currents. Monthly CO2 fluxes of 64 sub-continental regions, between June 2009 and May 2010, were estimated from GOSAT FTS SWIR Level 2 XCO2 retrievals (ver. 02.00) gridded to 5° × 5° cells and averaged on a monthly basis and monthly-mean GLOBALVIEW-CO2 data. Our result indicated that adding the GOSAT XCO2 retrievals to the GLOBALVIEW data in the flux estimation brings changes to fluxes of tropics and other remote regions where the surface-based data are sparse. The uncertainties of these remote fluxes were reduced by as much as 60% through such addition. Optimized fluxes estimated for many of these regions, were brought closer to the prior fluxes by the addition of the GOSAT retrievals. In most of the regions and seasons considered here, the estimated fluxes fell within the range of natural flux variabilities estimated with the component models. © Author(s) 2013. CC Attribution 3.0 License.

The Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) simultaneously observes column abundances and profiles of CH4 in the same field of view, from the shortwave infrared (SWIR) and thermal infrared (TIR) bands, respectively. We compared CH4 column-averaged dry-air mole fractions (XCH4) derived from the SWIR band, XCH4 calculated from the TIR CH4 profiles, and XCH4 calculated from the CH4 data obtained over Guam airport by commercial aircraft. The difference between the SWIR-XCH4 and aircraft XCH4 values (SWIR - aircraft) was -8 ppbv on average, and the 1 sigma standard deviation was 10 ppbv. The average difference between the TIR-XCH4 and aircraft XCH4 values (TIR - aircraft) was -5 ppbv, and the 1s standard deviation was 15 ppbv. The ranges of uncertainties in the calculated aircraft XCH4 values were estimated to be 9, 3, and 2 ppbv, which came from stratospheric CH4 assumption, tropopause height determination, and meteorological dataset used, respectively. Both the SWIR- and TIR-XCH4 values agreed within 0.5% of the aircraft XCH4 values, demonstrating that the GOSAT CH4 data are both valid and consistent with each other over the tropical ocean.

The Greenhouse gases Observing SATellite (GOSAT) was launched on 23 January 2009 to monitor the global distributions of carbon dioxide and methane from space. It has operated continuously since then. Here, we describe a retrieval algorithm for column abundances of these gases from the short-wavelength infrared spectra obtained by the Thermal And Near infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS). The algorithm consists of three steps. First, cloud-free observational scenes are selected by several cloud-detection methods. Then, column abundances of carbon dioxide and methane are retrieved based on the optimal estimation method. Finally, the retrieval quality is examined to exclude low-quality and/or aerosol-contaminated results. Most of the retrieval random errors come from instrumental noise. The interferences due to auxiliary parameters retrieved simultaneously with gas abundances are small. The evaluated precisions of the retrieved column abundances for single observations are less than 1% in most cases. The interhemispherical differences and temporal variation patterns of the retrieved column abundances show features similar to those of an atmospheric transport model.

The Greenhouse Gases Observing Satellite (GOSAT) will be launched in 2008 for global observations of greenhouse gases such as CO₂ and methane. This study examines the feasibility of retrieving CO₂ vertical profiles from spectra from 700 to 800cm^-1 (referred to as “CO₂ 15-μm band”) of the GOSAT/Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS band 4. Retrieval simulations in which the non-linear maximum a posteriori (MAP) method was applied to pseudo-spectra at CO₂ 15-μm band (signal to noise ratio of 300) showed that retrieved CO₂ profiles agreed with true CO₂ profiles to within ±1% above 800hPa without strongly depending on a priori when atmospheric conditions such as temperature were known. These simulation results confirm the validity of CO₂ retrieval at CO₂ 15-μm band of TANSO-FTS. Differences between retrieved and true CO₂ concentrations greatly increased if atmospheric data, especially temperature data, used in the retrieval included some bias and random errors; even 1K bias of temperature yielded as many as 9% bias in retrieved CO₂ concentrations. However, treating such uncertainties in atmospheric conditions as part of measurement noise and selecting retrieval channels on the basis of the information contents of CO₂ retrieval could reduce the discrepancies between retrieved and true CO₂ concentrations; the effect of bias in atmospheric conditions decreased by almost half and that of the random error decreased to be negligible. Furthermore, channel selection could cut the computational cost for the retrieval depending on the number of the selected channels. It depends on seasons and regions how many channels should be selected in the retrieval; therefore, it should be needed to examine the method of the channel selection for each atmospheric condition.

The Improved Limb Atmospheric Spectrometer-II (ILAS-II) is a satellite-borne solar occultation sensor onboard the Advanced Earth Observing Satellite-II (ADEOS-II). The ILAS-II succeeded the ILAS, The ILAS-II used four grating spectrometers to observe vertical profiles of gas volume mixing ratios of trace constituents and was also equipped with a Sun-edge sensor to determine tangent heights geometrically with high precision. The accuracy of gas volume mixing ratios depends on the accuracy of the tangent height determination. The combination method is a tangent height registration method that was developed to give appropriate tangent heights for the ILAS-II Version 1.4 data retrieval algorithm. This study describes the method used in the ILAS-II Version 1.4 retrieval algorithm to register tangent heights. The root-sum-square total random error is estimated to be 30 m, and the total systematic error is 180 m at an altitude of 30 km. The influence of the tangent height errors on the vertical profiles of gas volume mixing ratios in ILAS-II Version 1.4 is estimated by using the relative difference. The relative difference for each species is within 7% (20%) for an altitude shift of ±100 m (±300 m). © 2007 Optical Society of America.

The Improved Limb Atmospheric Spectrometer (ILAS)-II on board the Advanced Earth Observing Satellite (ADEOS)-II observed stratospheric aerosol in visible/near-infrared/infrared spectra over high latitudes in the Northern and Southern hemispheres, intermittently from January to March and continuously from April through October 2003. This study assesses the data quality of ILAS-II version 1.4 (V1.4) aerosol extinction coefficient at 780 nm. In the Northern Hemisphere (NH), aerosol extinction coefficient (AEC) from ILAS-II agreed with extinctions from SAGE H and SAGE III within ±10% and with extinction from POAM III within ±15% at heights below 20 km. From 20 to 26 km, ILAS-II AEC was smaller than extinctions from the other three sensors differences between ILAS-II and SAGE II ranged from 10% at 20 km to 34% at 26 km in the NH. Over the Southern Hemisphere (SH), ILAS-II AEC from 20 to 25 km in February was 12-66% below SAGE II extinction. The difference increased with increasing altitude. Comparisons between ILAS-II and POAM III from January to May in the SH ("non-PSC season") yielded qualitatively similar results. From June to October ("PSC season"), ILAS-II extinction was also smaller than POAM III extinction above 17 km however, ILAS-II extinction agreed with POAM III extinction to within ±15% from 12 to 17 km during the PSC season. The comparisons indicate that in both hemispheres the ILAS-II V1.4 AEC is comparable to extinctions from other measurements below approximately 20 km and systematically low above approximately 20 km although the mean difference is as small as ∼2 × 10&lt sup&gt -5&lt /sup&gt km&lt sup&gt -1&lt /sup&gt during the non-PSC season. Copyright 2006 by the American Geophysical Union.

[1] This paper presents initial results of simultaneous gas and aerosol retrievals from Improved Limb Atmospheric Spectrometer (ILAS) observations taken between November 1996 and June 1997. The solar occultation measurements were processed by an inversion method that included aerosol physical modeling and permitted simultaneous retrieval of O-3, HNO3, CH4, H2O, NO2, N2O, N2O5, ClONO2, CFC-11, and CFC-12 trace species and particle volume size distributions for key aerosol/polar stratospheric cloud (PSC) components such as liquid ternary solution, nitric acid trihydrate, nitric acid dihydrate, and water ice. The retrieval method was designed for the version 7.0 ILAS data processing algorithm. Gas retrieval results for the entire ILAS data set were compared to results from the earlier version 6.0 retrieval algorithm that was based on aerosol/PSC contribution estimates in the gas window channel. Gas data from nearby balloon-borne validation measurements and new data on the aerosol retrievals helped explain discrepancies between the two algorithms. The new version 7.0 methodology proved effective for simultaneous retrievals of all trace gases and showed a significant advantage when retrieving CH4, H2O, NO2, N2O, and CFC-12 from PSC observations.

Chemical ozone loss rates were estimated for the Arctic stratospheric vortex by using ozone profile data (Version 3.10) obtained with the Improved Limb Atmospheric Spectrometer (ILAS) for the spring of 1997. The analysis method is similar to the Match technique, in which an air parcel that the ILAS sounded twice at different locations and at different times was searched from the ILAS data set, and an ozone change rate was calculated from the two profiles. A statistical analysis indicates that the maximum ozone loss rate was found on the 450 K potential temperature surface in February, amounting to 84 ppbv/day. The integrated ozone loss for two months from February to March 1997 showed its maximum of 1.5+/-0.1 ppmv at the surface that followed the diabatic descent of the air parcels and reached the 425 K level on March 31. This is about 50% of the initial (February 1) ozone concentration. The present study demonstrated that data from a solar occultation sensor with a moderate altitude resolution can be used for the Match analysis.

The four wave-length extinction coefficients for stratospheric aerosols observed with SAGEII (Stratospheric Aerosol and Gas Experiment II) were analyzed. SAGEII is an occultation sensor which have measured aerosol extinction at 1.02, 0.525, 0.453, 0.385, um from 10 km up to 45 km in altitude since 1985. The eleven years data record from 1985 through 1995 shows the strong increase in the extinction in 1991 after the eruption Pinatubo (June 1991, Philippine). The wavelength dependence of the extinction coefficients also significantly changed after the eruption, showing the existence of large particles. Even in the background period before the eruption, the wavelength dependence show significant spatial variation. The wavelength dependence would be good information to investigate micro-physical evolution processes of aerosol particles along with the transportation in the lower stratosphere.

Temperature and ozone profiles were observed with the NIES ozone lidar system during the DYANA campaign from January 15th to March 15th at Tsukuba (36-degrees-E, 140-degrees-N). Temperature profiles from 30 km to 90 km and ozone profiles from 20 km to 45 km were obtained. The mesospheric temperature profiles were highly variable and deviations from the NASA88 model atmosphere were large in January and February, but the deviations were small in March 1990. Especially, conspicuous variations of the mesospheric temperature profiles, rapid increase around 55 km and rapid decrease around 75 km, were observed during the period from January 24th to 26th. We also observed layers with large vertical temperature gradients close to the adiabatic lapse rate above clear inversion layers in the middle mesosphere on January 17th and February 17th. The observed ozone number densities were > 10% lower than the ozone sonde data averaged over 22 years at 25 km and 30 km on January 17th, 25th, 26th. The deviations from the averaged ozone sonde data were small on February 5th, March 8th, 10th and 13th.