Search

Column-averaged dry air mole fractions of carbon dioxide and methane (XCO2 and XCH4) retrieved from SWIR (Short-Wavelength InfraRed) observations by GOSAT (Greenhouse Gases Observing SATellite) since 2009 and its successor GOSAT-2 since 2019 are available from NIES (National Institute for Environmental Studies) as SWIR L2 (Level 2) products. This paper shows the current status of the data quality of NIES SWIR L2 products and inter-satellite comparison results. Comparisons of XCO2 and XCH4 obtained from each satellite with ground-based observations from the TCCON (Total Carbon Column Observing Network) reveal that the averaged single measurement precision are less than 1.9 ppm and 10 ppb, respectively, and the site-to-site biases are less than 0.9 ppm and 5 ppb, respectively. The standard deviations of the inter-satellite differences for XCO2 and XCH4 are 2.18 ppm and 12.1 ppb, respectively, on an individual-data basis and 1.77 ppm and 11.7 ppb, respectively, on a monthly-regional-mean basis. While further improvements in the retrieval algorithm and bias-correction method are needed, GOSAT and GOSAT-2 retrievals are generally in good agreement. [ABSTRACT FROM AUTHOR]

In this study, we employ a regional inverse modelling approach to estimate monthly carbon fluxes over the Australian continent for 2015–2019 using the assimilation of the total column-averaged mole fractions of carbon dioxide from the Orbiting Carbon Observatory-2 (OCO-2, version 9) satellite. Subsequently, we study the carbon cycle variations and relate their fluctuations to anomalies in vegetation productivity and climate drivers. Our 5-year regional carbon flux inversion suggests that Australia was a carbon sink averaging - 0.46 ± 0.08 PgC yr −1 (excluding fossil fuel emissions), largely influenced by a strong carbon uptake (- 1.04 PgC yr −1) recorded in 2016. Australia's semi-arid ecosystems, such as sparsely vegetated regions (in central Australia) and savanna (in northern Australia), were the main contributors to the carbon uptake in 2016. These regions showed relatively high vegetation productivity, high rainfall, and low temperature in 2016. In contrast to the large carbon sink found in 2016, the large carbon outgassing recorded in 2019 coincides with an unprecedented rainfall deficit and higher-than-average temperatures across Australia. Comparison of the posterior column-averaged CO2 concentration with Total Carbon Column Observing Network (TCCON) stations and in situ measurements offers limited insight into the fluxes assimilated with OCO-2. However, the lack of these monitoring stations across Australia, mainly over ecosystems such as savanna and areas with sparse vegetation, impedes us from providing strong conclusions. To a certain extent, we found that the flux anomalies across Australia are consistent with the ensemble means of the OCO-2 Model Intercomparison Project (OCO-2 MIP) and FLUXCOM (2015–2018), which estimate an anomalous carbon sink for Australia in 2016 of - 1.09 and - 0.42 PgC yr −1 respectively. More accurate estimates of OCO-2 retrievals, with the addition of ocean glint data into our system, and a better understanding of the error in the atmospheric transport modelling will yield further insights into the difference in the magnitude of our carbon flux estimates. [ABSTRACT FROM AUTHOR]

In this study, we present column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) from a recently established measurement site in Gobabeb, Namibia. Gobabeb is a hyperarid desert site at the sharp transition zone between the sand desert and the gravel plains, offering unique characteristics with respect to surface albedo properties. Measurements started in January 2015 and are performed utilizing a ground-based Fourier transform infrared (FTIR) EM27/SUN spectrometer of the COllaborative Carbon Column Observing Network (COCCON). Gobabeb is the first measurement site observing XCO2 and XCH4 on the African mainland and improves the global coverage of ground-based remote-sensing sites. In order to achieve the high level of precision and accuracy necessary for meaningful greenhouse gas observations, we performed calibration measurements for 8 d between November 2015 and March 2016 with the COCCON reference EM27/SUN spectrometer operated at the Karlsruhe Institute of Technology. We derived scaling factors for XCO2 , XCH4 and XCO with respect to the reference instrument that are close to 1.0. We compare the results obtained in Gobabeb to measurements from the Total Carbon Column Observing Network (TCCON) sites at Réunion Island and Lauder. We choose these TCCON sites because, while 4000 km apart, the instruments at Gobabeb and Réunion Island operate at roughly the same latitude. The Lauder station is the southernmost TCCON station and functions as a background site without a pronounced XCO2 seasonal cycle. We find a good agreement for the absolute X gas values, apart from an expected XCH4 offset between Gobabeb and Lauder due to significantly different tropopause height, as well as representative intraday variability between TCCON and COCCON. Together with the absence of long-term drifts, this highlights the quality of the COCCON measurements. In the southern hemispheric summer, we observe lower XCO2 values at Gobabeb compared to the TCCON stations, likely due to the influence of the African biosphere. We performed coincident measurements with the Greenhouse Gases Observing Satellite (GOSAT), where GOSAT observed three nearby specific observation points, over the sand desert south of the station, directly over Gobabeb and over the gravel plains to the north. GOSAT H-gain XCO2 and XCH4 agree with the EM27/SUN measurements within the 1 σ uncertainty limit. The number of coincident soundings is limited, but we confirm a bias of 1.2–2.6 ppm between GOSAT M-gain and H-gain XCO2 retrievals depending on the target point. This is in agreement with results reported by a previous study and the GOSAT validation team. We also report a bias of 5.9–9.8 ppb between GOSAT M-gain and H-gain XCH4 measurements which is within the range given by the GOSAT validation team. Finally, we use the COCCON measurements to evaluate inversion-optimized CAMS model data. For XCO2 , we find high biases of 0.9 ± 0.5 ppm for the Orbiting Carbon Observatory-2 (OCO-2) assimilated product and 1.1 ± 0.6 ppm for the in situ-driven product with R2 > 0.9 in both cases. These biases are comparable to reported offsets between the model and TCCON data. The OCO-2 assimilated model product is able to reproduce the drawdown of XCO2 observed by the COCCON instrument at the beginning of 2017, as opposed to the in situ-optimized product. Also, for XCH4 , the observed biases are in line with prior model comparisons with TCCON. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Space-borne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on the Optimal Estimation algorithm, relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry air mole fraction of carbon dioxide (XCO2) from a sample of measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission. Those have been selected as a compromise between coverage and the lowest aerosol content possible, so that the impact of scattering particles can be neglected, for computational time purposes. For air masses below 3.0, 5AI XCO2 retrievals successfully capture the latitudinal variations of CO2 and its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a bias of 1.30±1.32 ppm (parts per million), which is comparable to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products over the same set of soundings. These nonscattering 5AI results, however, exhibit an average difference of about 3 ppm compared to ACOS results. We show that neglecting scattering particles for computational time purposes can explain most of this difference that can be fully corrected by adding to OCO-2 measurements an average calculated–observed spectral residual correction, which encompasses all the inverse setup and forward differences between 5AI and ACOS. These comparisons show the reliability of 5AI as an optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry air mole fractions of greenhouse gases. [ABSTRACT FROM AUTHOR]

We describe the change in operational instrument for the routine measurement of column-averaged dry-air mole fraction of several greenhouse gases (denoted Xgas) at the Lauder Total Carbon Column Observing Network (TCCON) site and the steps taken to demonstrate comparability between the two observation systems following a systematic methodology. Further, we intercompare retrieved Xgas values during an intensive intercomparison period during October and November 2018, when both instruments were performing optimally, and on subsequent, less frequent occasions. The average difference between the two observing systems was found to be well below the expected level of uncertainty for TCCON retrievals for all compared species. In the case of XCO2 the average difference was 0.0264±0.0465 % (0.11±0.19 µ mol mol -1). [ABSTRACT FROM AUTHOR]

In this study we present column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) from a recently established measurement site in Gobabeb, Namibia. Gobabeb is a hyperarid desert site at the sharp transition zone between the sand desert and the gravel plains, offering unique characteristics with respect to surface albedo properties. Measurements started January 2015 and are performed utilizing a ground-based Fourier transform infrared (FTIR) EM27/SUN spectrometer of the COllaborative Carbon Column Observing Network (COCCON). Gobabeb is the first measurement site observing XCO2 and XCH4 on the African mainland and improves the global coverage of ground-based remote-sensing sites. In order to achieve the high level of precision and accuracy necessary for meaningful greenhouse gas observations, we performed calibration measurements for eight days between November 2015 and March 2016 with the COCCON reference EM27/SUN spectrometer operated at the Karlsruhe Institute of Technology. We derived scaling factors for XCO2, XCH4 and XCO with respect to the reference instrument that are close to 1.0. We compare the results obtained in Gobabeb to measurements at Reunion Island and Lauder from the Total Carbon Column Observing Network (TCCON). We choose these TCCON sites because, while 4000 km apart, the instruments at Gobabeb and Reunion Island operate at roughly the same latitude. The Lauder station is the southernmost TCCON station and functions as a background site without a pronounced XCO2 seasonal cycle. We find a good agreement for the absolute Xgas values and representative diurnal variability. Together with the absence of long term drifts this highlights the quality of the COCCON measurements. In Southern hemispheric summer we observe lower XCO2 values at Gobabeb compared to the TCCON stations, likely due to the influence of the African biosphere. We performed coincident measurements with the Greenhouse Gases Observing Satellite (GOSAT), where GOSAT observed three nearby specific observation points, over the sand desert south of the station, directly over Gobabeb and over the gravel plains to the north. GOSAT H-gain XCO2 and XCH4 agree with the EM27/SUN measurements within the 1s uncertainty limit. The number of coincidence soundings is limited, but we confirm a bias of 1.2 - 2.6 ppm between GOSAT M-gain and H-gain XCO2 soundings depending on the target point. This is in agreement with results reported by a previous study and the GOSAT validation team. We also report a bias of 5.9 - 9.8 ppb between GOSAT M-gain and H-gain XCH4 measurements which is within the range given by the GOSAT validation team. Finally we use the COCCON measurements to evaluate inversion-optimized CAMS model data. For XCO2 we find high biases of 0.9 ± 0.5 ppm for the OCO-2 assimilated product and 1.1 ± 0.6 ppm for the in situ-driven product with R² > 0.9 in both cases. These biases are comparable to reported offsets between the model and TCCON data. The OCO-2 assimilated model product is able to reproduce the drawdown of XCO2 observed by the COCCON instrument beginning of 2017, opposed to the in situ-optimized product. Also for XCH4 the observed biases are in line with prior model comparisons with TCCON. [ABSTRACT FROM AUTHOR]

A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O2 A-band. For airmasses below 3.0, XCO2 retrievals successfully capture the latitudinal variations of CO2, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO2 Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated - observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases. [ABSTRACT FROM AUTHOR]

We describe the change of operational instrument for the routine measurement of column-averaged dry-air mole fraction of several greenhouse gases (denoted 푋gas) at the Lauder Total Carbon Column Observing Network (TCCON) site and the steps taken to demonstrate comparability between the two observation systems following a systematic methodology Further, we intercompare retrieved 푋gas values during an intensive intercomparison period during October and November 2018, when both instruments were performing optimally, and on subsequent, less frequent occasions. The average difference between the two observing systems was found to be well below the expected level of uncertainty for TCCON retrievals for all compared species. In the case of 푋 퐶푂2 the average difference was 0.0264 ± 0.0465% (0.11 ± 0.19µmolmol-1). [ABSTRACT FROM AUTHOR]

Column-averaged dry-air mole fraction of CO (X CO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometer networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC X CO measurements are investigated and discussed based on six NDACC–TCCON sites using data over the period 2007–2017. A direct comparison shows that the NDACC X CO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and the absolute bias between the NDACC and TCCON data is within 2 % at Saint-Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON X CO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error varies in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error is significant because it is higher than the reported uncertainty, particularly at Southern Hemisphere sites. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC X CO measurements become more consistent (5.6 %–8.5 %) with a mean value of 6.8 % at these sites. To determine the sources of the remaining bias, regular AirCore measurements at Orléans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON X CO measurements are 6.1 ± 1.6 % and 8.0 ± 3.2 % smaller than the AirCore measurements at Orléans and Sodankylä, respectively, indicating that the scaling factor of TCCON X CO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON X CO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR X CO data, and especially TCCON X CO data, with model or satellite data. [ABSTRACT FROM AUTHOR]

Column-averaged dry-air mole fraction of CO (XCO) measurements are obtained from two ground-based Fourier transform infrared (FTIR) spectrometers networks: the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). In this study, the differences between the TCCON and NDACC XCO measurements are investigated and discussed based on six NDACC/TCCON sites using data over the period 2007-2017. The NDACC XCO measurements are about 5.5 % larger than the TCCON data at Ny-Ålesund, Bremen, and Izaña (Northern Hemisphere), and about 0.3 % larger than the TCCON data at St Denis, Wollongong and Lauder (Southern Hemisphere). The hemispheric dependence of the bias is mainly attributed to their smoothing errors. The systematic smoothing error of the TCCON XCO data varies in the range between 0.2 % (Bremen) and 7.9 % (Lauder), and the random smoothing error in the range between 2.0 % and 3.6 %. The systematic smoothing error of NDACC data is between 0.1 % and 0.8 %, and the random smoothing error of NDACC data is about 0.3 %. For TCCON data, the smoothing error can be significant in that it is much higher than the reported uncertainty for TCCON XCO. To reduce the influence from the a priori profiles and different vertical sensitivities, the scaled NDACC a priori profiles are used as the common a priori profiles for comparing TCCON and NDACC retrievals. As a result, the biases between TCCON and NDACC XCO measurements become more consistent (5.6-8.5 %) with a mean value of 6.8 % at these sites. To understand the remaining bias, regular AirCore measurements at Orleans and Sodankylä are compared to co-located TCCON measurements. It is found that TCCON XCO measurements are 6.0 ± 1.9 % and 6.9 ± 2.5 % smaller than the AirCore measurements at Orleans and Sodankylä respectively, indicating that the scaling factor of TCCON XCO data should be around 1.0000 instead of 1.0672. Further investigations should be carried out in the TCCON community to determine the correct scaling factor to be applied to the TCCON XCO data. This paper also demonstrates that the smoothing error must be taken into account when comparing FTIR XCO data, and especially TCCON XCO data, with model or satellite data. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is an important atmospheric constituent affecting air quality and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR) combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with unprecedented level of detail on a global scale introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified DOAS (WFM-DOAS). We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine learning-based quality filter and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier Transform Spectrometer (FTS) measurements providing realistic error estimates of the satellite data: The XCO data set is characterised by a random error of 5.1 ppb (5.7 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.4 ppb (0.2 %). The natural XCO and XCH4 variations are well captured by the satellite retrievals, which is demonstrated by a high correlation to the reference data (R = 0.97 for XCO and R = 0.91 for XCH4 based on daily averages). We also present selected results from mission start until end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector. [ABSTRACT FROM AUTHOR]

The proxy method, using the ratio of total column CH4 to CO2 to reduce the effects of common biases, has been used to retrieve column-averaged dry-air mole fraction of CH4 from satellite data. The present study characterizes the remaining scattering effects in the CH4/CO2 ratio component of the Greenhouse gases Observing SATellite (GOSAT) retrieval and uses them for bias correction. The variation of bias between the GOSAT and Total Carbon Column Observing Network (TCCON) ratio component with GOSAT data-derived variables was investigated. Then, it was revealed that the variability of the bias could be reduced by using four variables for the bias correction—namely, airmass, 2 μm band radiance normalized with its noise level, the ratio between the partial column-averaged dry-air mole fraction of CH4 for the lower atmosphere and that for the upper atmosphere, and the difference in surface albedo between the CH4 and CO2 bands. The ratio of partial column CH4 reduced the dependence of bias on the cloud fraction and the difference between hemispheres. In addition to the reduction of bias (from 0.43% to 0%), the precision (standard deviation of the difference between GOSAT and TCCON) was reduced from 0.61% to 0.55% by the correction. The bias and its temporal variation were reduced for each site: the mean and standard deviation of the mean bias for individual seasons were within 0.2% for most of the sites. [ABSTRACT FROM AUTHOR]