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This study presents results from the European Centre for Medium-RangeWeather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of columnaveraged 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.

Top‐down estimates of CO₂ fluxes are typically constrained by either surface‐based or space‐based CO₂ observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to differences in inferred fluxes. Assimilating both surface‐based and space‐based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling related artifacts. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of 6‐year (2010–2015) CO₂ flux inversions. Flux inversions are performed assimilating surface‐based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space‐based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three data sets combined. Combining the data sets results in more precise flux estimates for subcontinental regions relative to any of the data sets alone. Combining the data sets also improves the accuracy of the posterior fluxes, based on reduced root‐mean‐square differences between posterior flux‐simulated CO₂ and aircraft‐based CO₂ over midlatitude regions (0.33–0.56 ppm) in comparison to GOSAT (0.37–0.61 ppm), TCCON (0.50–0.68 ppm), or in situ and flask measurements (0.46–0.56 ppm) alone. These results suggest that surface‐based and GOSAT measurements give complementary constraints on CO₂ fluxes in the northern extratropics and can be combined in flux inversions to improve constraints on regional fluxes. This stands in contrast with many earlier attempts to combine these data sets and suggests that improvements in the NASA Atmospheric CO₂ Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space‐based and surface‐based flux constraints.

Global measurements of atmospheric water vapour isotopologues aid to better understand the hydrological cycle and improve global circulation models. This paper presents a new data set of vertical column densities of H2O and HDO retrieved from short-wave infrared (2.3 µm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) onboard 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, and strict cloud filtering is therefore 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 ground-based measurements by the Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) project for data prior to 2014 (where MUSICA data are 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 co-located TROPOMI observations is good with an average bias of (-0.2±3)×1021 molec cm−2 ((1.1±7.2) %) in H2O and (-2±7)×1017 molec cm−2 ((-1.1±7.3) %) in HDO, which corresponds to a mean bias of (-14±17) ‰ in a posteriori δD. The bias is lower at low- and mid-latitude stations and higher at high-latitude stations. 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.

The thermal And near infrared sensor for carbon observations Fourier transform spectrometer (TANSO-FTS) on board the Greenhouse Gases Observing Satellite (GOSAT) applies the normal nadir mode above the land (“land data”) and sun glint mode over the ocean (“ocean data”) to provide global distributions of column-averaged dry-air mole fractions of CO2 and CH4, or XCO2 and XCH4. Several algorithms have been developed to obtain highly accurate greenhouse gas concentrations from TANSO-FTS/GOSAT spectra. So far, all the retrieval algorithms have been validated with the measurements from ground-based Fourier transform spectrometers from the Total Carbon Column Observing Network (TCCON), but limited to the land data. In this paper, the ocean data of the SRPR, SRFP (the proxy and full-physics versions 2.3.5 of SRON/KIT's RemoTeC algorithm), NIES (National Institute for Environmental Studies operational algorithm version 02.21) and ACOS (NASA's Atmospheric CO2 Observations from Space version 3.5) are compared with FTIR measurements from five TCCON sites and nearby GOSAT land data.For XCO2, both land and ocean data of NIES, SRFP and ACOS show good agreement with TCCON measurements. Averaged over all TCCON sites, the relative biases of ocean data and land data are −0.33 and −0.13 % for NIES, 0.03 and 0.04 % for SRFP, 0.06 and −0.03 % for ACOS, respectively. The relative scatter ranges between 0.31 and 0.49 %. For XCH4, the relative bias of ocean data is even less than that of the land data for the NIES (0.02 vs. −0.35 %), SRFP (0.04 vs. 0.20 %) and SRPR (−0.02 vs. 0.06 %) algorithms. Compared to the results for XCO2, the XCH4 retrievals show larger relative scatter (0.65–0.81 %).

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.

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.

NASA's Orbiting Carbon Observatory-2 (OCO-2) has been measuring carbon dioxide column-averaged dry-air mole fraction, X$_{CO_{2}}$, in the Earth's atmosphere for over 2 years. In this paper, we describe the comparisons between the first major release of the OCO-2 retrieval algorithm (B7r) and X$_{CO_{2}}$ from OCO-2's primary ground-based validation network: the Total Carbon Column Observing Network (TCCON). The OCO-2 X$_{CO_{2}}$ retrievals, after filtering and bias correction, agree well when aggregated around and coincident with TCCON data in nadir, glint, and target observation modes, with absolute median differences less than 0.4 ppm and RMS differences less than 1.5 ppm. After bias correction, residual biases remain. These biases appear to depend on latitude, surface properties, and scattering by aerosols. It is thus crucial to continue measurement comparisons with TCCON to monitor and evaluate the OCO-2 X$_{CO_{2}}$ data quality throughout its mission.

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.

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. Recent work has shown that CO2 fluxes inferred from XCO2 data from the Japanese Greenhouse gases Observing SATellite (GOSAT) have a larger seasonal amplitude and a more negative annual net CO2 balance than those inferred from the in situ data. The causes of this enhanced European CO2 uptake have since become the focus of recent studies. We show this elevated uptake over Europe could largely be explained by mis-fitting data due to regional biases. We establish a reference in situ inversion that uses an Ensemble Kalman Filter (EnKF) to assimilate surface flask data and the XCO2 data from the surface-based Total Carbon Column Observing Network (TCCON). The same EnKF system is also used 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 1.18 ± 0.1 GtC a−1 compared to a value of 0.56 ± 0.1 GtC a−1 for our control inversion that uses only in situ data. To reconcile these two estimates, we have performed a series of numerical experiments that assimilate observations with biases or assimilate synthetic observations for which part or all of the GOSAT XCO2 data are replaced with model data. We find that 50–80% of the elevated European uptake in 2010 inferred from GOSAT data is due to retrievals outside the immediate European region, while most of the remainder can be explained by a sub-ppm retrieval bias over Europe. We have used data assimilation techniques to estimate monthly GOSAT XCO2 biases from the joint assimilation of in situ observations and GOSAT XCO2 retrievals. We find a monthly varying bias of up to 0.5 ppm can explain an overestimate of the annual sink of up to 0.18 GtC a−1.

This report describes a validation study of Greenhouse gases Observing Satellite (GOSAT) data processing using ground-based measurements of the Total Carbon Column Observing Network (TCCON) as reference data for column-averaged dry air mole fractions of atmospheric carbon dioxide (X CO2). We applied the photon path length probability density function method to validate X CO2 retrievals from GOSAT data obtained during 22months starting from June 2009. This method permitted direct evaluation of optical path modifications due to atmospheric light scattering that would have a negligible impact on ground-based TCCON measurements but could significantly affect gas retrievals when observing reflected sunlight from space. Our results reveal effects of optical path lengthening over Northern Hemispheric stations, essentially from May-September of each year, and of optical path shortening for sun-glint observations in tropical regions. These effects are supported by seasonal trends in aerosol optical depth derived from an offline threedimensional aerosol transport model and by cirrus optical depth derived from space-based measurements of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. Removal of observations that were highly contaminated by aerosol and cloud from the GOSAT data set resulted in acceptable agreement in the seasonal variability of X CO2 over each station as compared with TCCON measurements. Statistical comparisons between GOSAT and TCCON coincident measurements of CO 2 column abundance show a correlation coefficient of 0.85, standard deviation of 1.80ppm, and a sub-ppm negative bias of -0.43ppm for all TCCON stations. Global distributions of monthly mean retrieved X CO2 with a spatial resolution of 2.5° latitude×2.5° longitude show agreement within∼2.5°ppm with those predicted by the atmospheric tracer transport model.

In September/October 2009, six European ground-based Fourier Transform Spectrometers (FTS) of the Total Carbon Column Observation Network (TCCON) were calibrated for the first time using aircraft measurements. The campaign was part of the Infrastructure for Measurement of the European Carbon Cycle (IMECC) project. During this campaign, altitude profiles of several trace gases and meteorological parameters were taken close to the FTS sites (typically within 1–2 km distance for flight altitudes below 5000 m). Profiles of CO2, CH4, CO and H2O were measured continuously. N2O, H2, and SF6 were later derived from flask measurements. The aircraft data had a vertical coverage ranging from approximately 300 to 13 000 m, corresponding to ~80% of the total atmospheric column seen by the FTS. This study summarizes the calibration results for CH4. The resulting calibration factor of 0.978 ± 0.002 (±1 σ) from the IMECC campaign agreed very well with the results that Wunch et al. (2010) had derived for TCCON instruments in North America, Australia, New Zealand, and Japan using similar methods. By combining our results with the data of Wunch et al. (2010), the uncertainty of the calibration factor could be reduced by a factor of three (compared to using only IMECC or only Wunch et al. (2010) data). A careful analysis of the calibration method used by Wunch et al. (2010) revealed that the incomplete vertical coverage of the aircraft profiles can lead to a bias in the calibration factor. This bias can be compensated with a new iterative approach that we developed. Using this improved method, we derived a significantly lower calibration factor of 0.974 ± 0.002 (±1 σ). This corresponds to a correction of all TCCON CH4 measurements by roughly −7 ppb.

The Total Carbon Column Observing Network (TCCON) is a ground-based network of Fourier Transform Spectrometer (FTS) sites around the globe, where the column abundances of CO2, CH4, N2O, CO and O2 are measured. CO2 is constrained with a precision better than 0.25% (1-σ). To achieve a similarly high accuracy, calibration to World Meteorological Organization (WMO) standards is required. This paper introduces the first aircraft calibration campaign of five European TCCON sites and a mobile FTS instrument. A series of WMO standards in-situ profiles were obtained over European TCCON sites via aircraft and compared with retrievals of CO2 column amounts from the TCCON instruments. The results of the campaign show that the FTS measurements are consistently biased 1.1% ± 0.2% low with respect to WMO standards, in agreement with previous TCCON calibration campaigns. The standard a priori profile for the TCCON FTS retrievals is shown to not add a bias. The same calibration factor is generated using aircraft profiles as a priori and with the TCCON standard a priori. With a calibration to WMO standards, the highly precise TCCON CO2 measurements of total column concentrations provide a suitable database for the calibration and validation of nadir-viewing satellites.