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Space-based observations of carbon dioxide (CO2) are the backbone of the global and national-scale carbon monitoring systems that are currently being developed to support and verify greenhouse gas emission reduction measures. Current and planned public satellite missions, such as GOSAT 1+2, OCO 1-3 and the European Union's Anthropogenic Carbon Dioxide Monitoring mission CO2M, aim at constraining national and regional-scale emissions down to scales of urban agglomerations and large point sources with emissions in excess of ~10 MtCO2/year.We report on the DLR demonstrator mission CO2Image, which is planned for launch in 2026. The mission will complement the suite of planned CO2 sensors by zooming in on facility-scale emissions, detecting and quantifying emissions from point sources as small as 1 MtCO2/year. A fleet of CO2Image sensors would be able to monitor roughly 90% of the CO2 emissions from coal-fired power plants worldwide. The key feature of the mission is a target region approach, measuring approximately 75 tiles of size ~50 x 50 km2 per day at a resolution of 50 x 50 m2. Thus, CO2Image will be able to resolve plumes from individual localized sources, essentially providing super-resolution nests for survey missions such as CO2M. In addition, the choice of the spectral window will allow the detection of point sources of methane as small as 100 kg CH4/hr will also be possible.We present the instrument concept, a spaceborne push-broom imaging grating spectrometer developed and built by DLR. It will measure spectra of reflected solar radiation in the short wave infrared spectral band around 2000 nm. It relies on a comparatively compact design with a single spectral window and a spectral resolution of approximately ~1 nm. This spectral resolution has been optimized for greenhouse gas retrieval and should provide improved precision and accuracy compared to hyperspectral sensors with comparable spatial resolution. We will further discuss the overall mission concept in terms of the sampling strategy, outlining how target scenes will be selected. As a publicly-funded mission, CO2Image will provide public, transparent information about anthropogenic greenhouse gas emissions from space.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over its first 11 years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inversion systems (models) which do not assimilate satellite-derived CO2. In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter, and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2×106 out of 37×106) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

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.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

CO2Image is a satellite demonstration mission, now in phase B, to be launched by the German Aerospace Center (DLR ) in 2026. The satellite will carry a next generation imaging spectrometer for measuring atmospheric column concentrations of carbon dioxide (CO2). The instrument concept reconciles compact design with fine ground resolution (50-100 m) with decent spectral resolution (1.0-1.3 nm) in the shortwave infrared spectral range (2,000 nm). Thus, CO2Image will enable quantification of point sources with CO2 emission rates of less than 1 MtCO2/a. This will complement global monitoring missions such as CO2M, which are less sensitive to point sources due to their coarser ground resolution, and hyperspectral imagers, which suffer from spectroscopic interference errors that limit the quantification of small sources due to their coarser spectral resolution. Further, CO2Image is sufficiently compact to envision, after successful demonstration, a fleet of sensors operated by public bodies to support and evaluate greenhouse gas emission reduction strategies on community-scales.Here, we will focus on the design choices and performance analyses carried out for building the mission. We have developed an end-to-end simulator that starts out with realistic atmospheric CO2 concentration fields simulated by large-eddy-simulations of CO2 plumes emitted by various point-sources in a 50x50 km2 tile. The latter represents a typical individual target which CO2Image will sample in less than 100 ms in a pushbroom configuration using forward motion compensation. The simulated concentration fields will be used to create synthetic measurements from instrument parameters. These measurements will then be fed into our retrieval and we will use mass balance methods to estimate the compatible emission rates. We evaluate various performance parameters and hypothetical error sources such as calibration errors in terms of their impact on the emission estimates.

Recently, the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm has been applied to measurements of the Greenhouse gases Observing SATellite (GOSAT) and its successor GOSAT-2. FOCAL has been originally developed for Orbiting Carbon Observatory-2 (OCO-2) retrievals with the focus on the derivation of carbon dioxide (XCO2). However, depending on the available spectral windows, FOCAL also successfully retrieves total column amounts for other atmospheric species. Here, we show new results from updated GOSAT and GOSAT-2 FOCAL retrievals. The main focus is placed on methane (XCH4; full physics and proxy product), water vapour (XH2O) and the relative ratio of semi-heavy water (HDO) to water vapour (δD). Due to the extended spectral range of GOSAT-2 it is also possible to derive information on carbon monoxide (XCO) and nitrous oxide (XN2O) for which we also show first results. We also present an update on XCO2 from both instruments. Compared to the previous product version (v1), the number of valid XCO2 data could be significantly increased in the updated version (v3.0) by 50 % for GOSAT and about a factor of two for GOSAT-2. All FOCAL data products show reasonable spatial distribution and temporal variations. Comparisons with TCCON (Total Carbon Column Observing Network) result in station-to-station biases which are generally in line with the reported TCCON uncertainties. With this updated version of the GOSAT-2 FOCAL data, we provide a first total column average XN2O product. Global XN2O maps show a gradient from the tropics to higher latitudes in the order of 15 ppb, which can be explained by variations in tropopause height. The new GOSAT-2 XN2O product compares well with TCCON. Its station-to-station variability is lower than 2 ppb, which is about the magnitude of the typical N2O variations close to the surface. However, both GOSAT-2 and TCCON measurements show that the seasonal variations in the total column average XN2O are in the order of 8 ppb peak-to-peak, which can be easily resolved by the GOSAT-2 FOCAL data.

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

Since 2009, the Greenhouse gases Observing SATellite (GOSAT) performs radiance measurements in the shortwave-infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 are also available. We present first results from the application of the Fast atmOspheric traCe gAs retrieval (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL has initially been developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast it is currently considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission, to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This includes particularly modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT/GOSAT-2 is the independent use of both S and P polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT’s wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O) and solar induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semiheavy water vapour (HDO) and (in the case of GOSAT-2) also carbon monoxide (CO) total columns and possibly nitrous oxide (XN2O). Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal an agreement of long-term temporal variations within about 1 ppm over one decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a mean regional bias of the new GOSAT-FOCAL product to the ground based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results.

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.

This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, this data has been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of this data in order to highlight how this latest version may be used in the future. We describe in detail how the data is generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun-glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement to TCCON, with an overall correlation coefficient of 0.92 for the 88,345 co-located measurements. The single measurement precision is found to be 13.72 ppb and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4/XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 ppb to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. This data is available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).

We present inverse modelling (top down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonised in situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.8 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), total wetland emissions of 4.3 (2.3–8.2) Tg CH4 yr−1 from the EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Taking into account the wetland emissions from the WETCHIMP ensemble, the top-down estimates are broadly consistent with the sum of anthropogenic and natural bottom-up inventories. However, the contribution of natural sources and their regional distribution remain rather uncertain. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. The estimated average regional biases range between −40 and 20 % at the aircraft profile sites in France, Hungary and Poland.

We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations (

The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

This paper presents a new data set of vertical column densities of the water vapour isotopologues H2O and HDO retrieved from short-wave infrared (2.3 μm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km × 7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, thus strict cloud filtering is necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with measurements by the project Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) for data prior to 2014 (where MUSICA data is available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and collocated TROPOMI observations is good with an average bias of (0.02 ± 2) · 1021 molec cm−2 in H2O and (−0.3 ± 7) · 1017 molec cm−2 in HDO, which corresponds to a bias of (−12 ± 17) ‰ in a posteriori δD. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single overpass data.

Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra spacecraft were expected to have an accuracy of 10 % prior to launch in 1999. Here we evaluate MOPITT version 7 joint TIR-NIR (V7J) accuracy and precision, and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings, and ground based measurements. 1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. 2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. 3) Using a small region approximation (SRA) a new filtering scheme is developed and applied based on additional quality indicators such as signal-to-noise. After applying these new filters, the root mean squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 ppb to 2.55 ppb. 4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. 5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground and satellite based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO, and tend to pull concentrations away from the prior, and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7 ppm compared to the free run (1.1 and 1.4 ppm, respectively) and an improved estimated precision of 1 ppm compared to the GOSAT BESD data (3.3 ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00 UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000 km) even up to day 5 compared to its own analysis.

Estimates of the natural CO2 flux over Europe inferred from in situ measurements of atmospheric CO2 mole fraction have been used previously to check top-down flux estimates inferred from space-borne dry-air CO2 column (XCO2) retrievals. Several recent studies have shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) and the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) have larger seasonal amplitudes and a more negative annual net CO2 balance than those inferred from the in situ data. The cause of this elevated European uptake of CO2 is still unclear, but some recent studies have suggested that this is a genuine scientific phenomenon. Here, we put forward an alternative hypothesis and show that realistic levels of bias in GOSAT data can result in an erroneous estimate of elevated uptake over Europe. We use a global flux inversion system to examine the relationship between measurement biases and estimates of CO2 uptake from Europe. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate conventional surface mole fraction observations and XCO2 retrievals from the surface-based Total Carbon Column Observing Network (TCCON). We use the same EnKF system to assimilate two independent versions of GOSAT XCO2 data. We find that the GOSAT-inferred European terrestrial biosphere uptake peaks during the summer, similar to the reference inversion, but the net annual flux is 1.40 ± 0.19 GtC a−1 compared to a value of 0.58 ± 0.14 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we perform a series of numerical experiments that assimilate observations with added biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that for our global flux inversions, a large portion (60–90 %) of the elevated European uptake inferred from GOSAT data in 2010 is due to retrievals outside the immediate European region, while the remainder can largely be explained by a sub-ppm retrieval bias over Europe. We use a data assimilation approach to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. The inferred biases represent an estimate of systematic differences between GOSAT XCO2 retrievals and the inversion system at regional or sub-regional scales. We find that a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.20 GtC a−1. Our results highlight the sensitivity of CO2 flux estimates to regional observation biases, which have not been fully characterized by the current observation network. Without further dedicated measurements we cannot prove or disprove that European ecosystems are taking up a larger-than-expected amount of CO2. More robust inversion systems are also needed to infer consistent fluxes from multiple observation types.

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) satellite observations using the RemoTeC algorithm, previously successfully applied to retrieval of greenhouse gas concentration from the Greenhouse Gases Observing Satellite (GOSAT). The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for almost 2 years of OCO-2 data from September 2014 to July 2016. We found that fitting an additive radiometric offset in all three spectral bands of OCO-2 significantly improved the retrieval. Based on a small correlation of the XCO2 error over land with goodness of fit, we applied an a posteriori bias correction to our OCO-2 retrievals. In overpass averaged results, XCO2 retrievals have an SD of ∼ 1.30 ppm and a station-to-station variability of ∼ 0.40 ppm among collocated TCCON sites. The seasonal relative accuracy (SRA) has a value of 0.52 ppm. The validation shows relatively larger difference with TCCON over high-latitude areas and some specific regions like Japan.

We present inverse modelling (top-down) estimates of European methane (CH4) emissions for 2006–2012 based on a new quality-controlled and harmonized in-situ data set from 18 European atmospheric monitoring stations. We applied an ensemble of seven inverse models and performed four inversion experiments, investigating the impact of different sets of stations and the use of a priori information on emissions. The inverse models infer total CH4 emissions of 26.7 (20.2–29.7) Tg CH4 yr−1 (mean, 10th and 90th percentiles from all inversions) for the EU-28 for 2006–2012 from the four inversion experiments. For comparison, total anthropogenic CH4 emissions reported to UNFCCC (bottom-up, based on statistical data and emissions factors) amount to only 21.3 Tg CH4 yr−1 (2006) to 18.8 Tg CH4 yr−1 (2012). A potential explanation for the higher range of top-down estimates compared to bottom-up inventories could be the contribution from natural sources, such as peatlands, wetlands, and wet soils. Based on seven different wetland inventories from the Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP) total wetland emissions of 4.3 (2.3–8.2) CH4 yr−1 from EU-28 are estimated. The hypothesis of significant natural emissions is supported by the finding that several inverse models yield significant seasonal cycles of derived CH4 emissions with maxima in summer, while anthropogenic CH4 emissions are assumed to have much lower seasonal variability. Furthermore, we investigate potential biases in the inverse models by comparison with regular aircraft profiles at four European sites and with vertical profiles obtained during the Infrastructure for Measurement of the European Carbon Cycle (IMECC) aircraft campaign. We present a novel approach to estimate the biases in the derived emissions, based on the comparison of simulated and measured enhancements of CH4 compared to the background, integrated over the entire boundary layer and over the lower troposphere. This analysis identifies regional biases for several models at the aircraft profile sites in France, Hungary and Poland.

The TCCON (Total Carbon Column Observing Network) FTIR (Fourier transform infrared) network provides highly accurate observations of greenhouse gas column-averaged dry-air mole fractions. As an important component of TCCON quality assurance, sealed cells filled with approximately 5 mbar of HCl are used for instrumental line shape (ILS) monitoring at all TCCON sites. Here, we introduce a calibration procedure for the HCl cells which employs a refillable, pressure-monitored reference cell filled with C2H2. Using this method, we identify variations of HCl purity between the TCCON cells as a non-negligible disturbance. The new calibration procedure introduced here assigns effective pressure values to each individual cell to account for additional broadening of the HCl lines. This approach will improve the consistency of the network by significantly reducing possible station-to-station biases due to inconsistent ILS results from different HCl cells. We demonstrate that the proposed method is accurate enough to turn the ILS uncertainty into an error source of secondary importance from the viewpoint of network consistency.

Water vapour (H2O) is one of the operationally retrieved key species of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the Environmental Satellite (ENVISAT) which was launched into its sun-synchronous orbit on 1 March 2002 and operated until April 2012. Within the MIPAS validation activities, independent observations from balloons, aircraft, satellites, and ground-based stations have been compared to European Space Agency (ESA) version 4.61 operational H2O data comprising the time period from July 2002 until March 2004 where MIPAS measured with full spectral resolution. No significant bias in the MIPAS H2O data is seen in the lower stratosphere (above the hygropause) between about 15 and 30 km. Differences of H2O quantities observed by MIPAS and the validation instruments are mostly well within the combined total errors in this altitude region. In the upper stratosphere (above about 30 km), a tendency towards a small positive bias (up to about 10%) is present in the MIPAS data when compared to its balloon-borne counterpart MIPAS-B, to the satellite instruments HALOE (Halogen Occultation Experiment) and ACE-FTS (Atmospheric Chemistry Experiment, Fourier Transform Spectrometer), and to the millimeter-wave airborne sensor AMSOS (Airborne Microwave Stratospheric Observing System). In the mesosphere the situation is unclear due to the occurrence of different biases when comparing HALOE and ACE-FTS data. Pronounced deviations between MIPAS and the correlative instruments occur in the lowermost stratosphere and upper troposphere, a region where retrievals of H2O are most challenging. Altogether it can be concluded that MIPAS H2O profiles yield valuable information on the vertical distribution of H2O in the stratosphere with an overall accuracy of about 10 to 30% and a precision of typically 5 to 15% – well within the predicted error budget, showing that these global and continuous data are very valuable for scientific studies. However, in the region around the tropopause retrieved MIPAS H2O profiles are less reliable, suffering from a number of obstacles such as retrieval boundary and cloud effects, sharp vertical discontinuities, and frequent horizontal gradients in both temperature and H2O volume mixing ratio (VMR). Some profiles are characterized by retrieval instabilities.

We use the GEOS-Chem global 3-D model of atmospheric chemistry and transport and an ensemble Kalman filter to simultaneously infer regional fluxes of methane (CH4) and carbon dioxide (CO2) directly from GOSAT retrievals of XCH4:XCO2, using sparse ground-based CH4 and CO2 mole fraction data to anchor the ratio. This work builds on previously reported theory that takes advantage that: (1) these ratios are less prone to systematic error than either the full physics data products or the proxy CH4 data products; and (2) the resulting CH4 and CO2 fluxes are self-consistent. We show that a posteriori fluxes inferred from the GOSAT data generally outperform the fluxes inferred only from in situ data, as expected. GOSAT CH4 and CO2 fluxes are consistent with global growth rates for CO2 and CH4 reported by NOAA, and with a range of independent data including in particular new profile measurements (0–7 km) over the Amazon basin that were collected specifically to help validate GOSAT over this geographical region. We find that large-scale multi-year annual a posteriori CO2 fluxes inferred from GOSAT data are similar to those inferred from the in situ surface data but with smaller uncertainties, particularly over the tropics. GOSAT data are consistent with smaller peak-to-peak seasonal amplitudes of CO2 than either a priori or the in situ inversion, particularly over the tropics and the southern extra-tropics. Over the northern extra-tropics, GOSAT data show larger uptake than the a priori but less than the in situ inversion, resulting in small net emissions over the year. We also find evidence that the carbon balance of tropical South America was perturbed following the droughts of 2010 and 2012 with net annual fluxes not returning to an approximate annual balance until 2013. In contrast, GOSAT data significantly changed the a priori spatial distribution of CH4 emission with a 40 % increase over tropical South America and tropical Asia and smaller decrease over Eurasia and temperate South America. We find no evidence from GOSAT that tropical South American CH4 fluxes were dramatically affected by the two large-scale Amazon droughts. However, we find that GOSAT data are consistent with double seasonal peaks in fluxes that are reproduced over the five years we studied: a small peak in January to April and a larger peak in June to October, which is likely due to superimposed emissions from different geographical regions.

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry air mole fractions, XCO2, in the Earth’s atmosphere for almost two years. In this paper, we describe the comparisons between the OCO-2 version 7Br retrievals and XCO2 estimates from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 XCO2 retrievals, after bias correction, agree well globally with the TCCON for nadir, glint, and target observations, with median differences less than 0.5 ppm and RMS differences typically below 1.5 ppm. Target observations over TCCON stations correlate best with the TCCON data (R2 = 0.83) on a global scale. At local scales, the target comparisons reveal residual biases likely related to surface properties and aerosol scattering. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 XCO2 data quality throughout its mission.

Gobal methane emissions were estimated for 2000–2012 using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, the anthropogenic and biosphere emissions of CH4 are simultaneously constrained by global atmospheric in-situ methane mole fraction observations. We use three configurations developed in Tsuruta et al. (2016) to assess the sensitivity of the CH4 flux estimates to (a) the number of unknown flux scaling factors to be optimized which in turn depends on the choice of underlying land-ecosystem map, and (b) on the parametrization of vertical mixing in the 30 atmospheric transport model TM5. The posterior emission estimates were evaluated by comparing simulations to surface in-situ observation sites, to profile observations made by aircraft, to dry air total column-averaged mole fractions (XCH4) observations from the Total Carbon Column Observing Network (TCCON), and to XCH4 retrievals from the Greenhouse gases Observing SATellite (GOSAT). Our estimated posterior mean global total emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, and emission estimates during 2007–2012 are 18 Tg CH4 yr−1 greater than those from 2001–2006, mainly driven by an 35 increase in emissions from the south America temperate region, the Asia temperate region and Asia tropics. The sensitivity of the flux estimates to the underlying ecosystem map was large for the Asia temperate region and Australia, but not significant in the northern latitude regions, i.e. the north American boreal region, the north American temperate region and Europe. Instead, the posterior estimates for the northern latitude regions show larger sensitivity to the choice of convection scheme in TM5. The Gregory et al. (2000) mixing scheme with faster interhemispheric exchange leads to higher estimated CH4 emissions at northern latitudes, and lower emissions in southern latitudes, compared to the estimates using Tiedtke (1989) convection scheme. Our evaluation with non-assimilated observations showed that posterior mole fractions were better matched with the 5 observations when Gregory et al. (2000) convection scheme was used.

Water vapour has been measured from the International Scientific Station Jungfraujoch (ISSJ, 47° N, 7° E, 3580 m a.s.l.) during the winters of 1999/2000 and 2000/2001 by microwave radiometry and Raman lidar. The abundance of atmospheric water vapour between the planetary boundary layer and the upper stratosphere varies over more than three orders of magnitude. The currently used measurement techniques are suited to determine the abundance of water vapour in different atmospheric regimes, however none can resolve by itself a vertical distribution profile over a full altitude range from ground level to the top of the stratosphere. We present such a water vapour profile where simultaneous measurements from a Raman lidar and a microwave radiometer are combined to cover both the troposphere and the stratosphere, respectively. We also present a study of the stratospheric and tropospheric water vapour variability for the two consecutive winters.