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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.

The objective of this study is to derive methane (CH4) emissions from three landfills, which are found to be the most significant CH4 sources in the metropolitan area of Madrid in Spain. We derive CH4 emissions from the CH4 enhancements observed by spaceborne and ground-based instruments. We apply satellite-based measurements from the TROPOspheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) together with measurements from the ground-based COllaborative Carbon Column Observing Network (COCCON) instruments. In 2018, a 2-week field campaign for measuring the atmospheric concentrations of greenhouse gases was performed in Madrid in the framework of Monitoring of the Greenhouse Gases Concentrations in Madrid (MEGEI-MAD) project. Five COCCON instruments were deployed at different locations around the Madrid city center, enabling the observation of total column-averaged CH4 mixing ratios (XCH4). Considering the prevalent wind regimes, we calculate the wind-assigned XCH4 anomalies for two opposite wind directions. Pronounced bipolar plumes are found when applying the method to NO2, which implies that our method of wind-assigned anomaly is suitable to estimate enhancements of trace gases at the urban level from satellite-based measurements. For quantifying the CH4 emissions, the wind-assigned plume method is applied to the TROPOMI XCH4 and to the lower tropospheric CH4 / dry-air column ratio (TXCH4) of the combined TROPOMI+IASI product. As CH4 emission strength we estimate 7.4 × 1025 ± 6.4 × 1024 molec. s−1 from the TROPOMI XCH4 data and 7.1 × 1025 ± 1.0 × 1025 molec. s−1 from the TROPOMI+IASI merged TXCH4 data. We use COCCON observations to estimate the local source strength as an independent method. COCCON observations indicate a weaker CH4 emission strength of 3.7 × 1025 molec. s−1 from a local source (the Valdemingómez waste plant) based on observations from a single day. This strength is lower than the one derived from the satellite observations, and it is a plausible result. This is because the analysis of the satellite data refers to a larger area, covering further emission sources in the study region, whereas the signal observed by COCCON is generated by a nearby local source. All emission rates estimated from the different observations are significantly larger than the emission rates provided via the official Spanish Register of Emissions and Pollutant Sources.

High-resolution Fourier transform infrared (FTIR) solar observations are particularly relevant for climate studies, as they allow atmospheric gaseous composition and multiple climate processes to be monitored in detail. In this context, the present paper provides an overview of 20 years of FTIR measurements taken in the framework of the NDACC (Network for the Detection of Atmospheric Composition Change) from 1999 to 2018 at the subtropical Iza��a Observatory (IZO, Spain). Firstly, long-term instrumental performance is comprehensively assessed, corroborating the temporal stability and reliable instrumental characterization of the two FTIR spectrometers installed at IZO since 1999. Then, the time series of all trace gases contributing to NDACC at IZO are presented (i.e. C$_{2}$H$_{6}$, CH$_{4}$, ClONO$_{2}$, CO, HCl, HCN, H$_{2}$CO, HF, HNO$_{3}$, N$_{2}$O, NO$_{2}$, NO, O$_{3}$, carbonyl sulfide (OCS), and water vapour isotopologues H$_{2}$$^{16}$O, H$_{2}$$^{18}$O, and HD$^{16}$O), reviewing the major accomplishments drawn from these observations. In order to examine the quality and long-term consistency of the IZO FTIR observations, a comparison of those NDACC products for which other high-quality measurement techniques are available at IZO has been performed (i.e. CH$_{4}$, CO, H$_{2}$O, NO$_{2}$, N$_{2}$O, and O$_{3}$). This quality assessment was carried out on different timescales to examine what temporal signals are captured by the FTIR records, and to what extent. After 20 years of operation, the IZO NDACC FTIR observations have been found to be very consistent and reliable over time, demonstrating great potential for climate research. Long-term NDACC FTIR data sets, such as IZO, are indispensable tools for the investigation of atmospheric composition trends, multi-year phenomena, and complex climate feedback processes, as well as for the validation of past and present space-based missions and chemistry climate models.

Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions (

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.

TanSat is the 1st Chinese carbon dioxide (CO$_{2}$) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO$_{2}$ retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O$_{2}$ A band retrieval. Accordingly, we extend the previous TanSat single CO$_{2}$ weak band retrieval to a combined O$_{2}$ A and CO$_{2}$ weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO$_{2}$ retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO$_{2}$ retrieval. We show that our new approach produces a significant improvement on the XCO$_{2}$ retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of ���0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO$_{2}$ retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO$_{2}$ processing.

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.

Using five long-running ground-based atmospheric observatories in Europe, we demonstrate the utility of long-term, stationary, ground-based measurements of atmospheric total columns for verifying annual methane emission inventories. Our results indicate that the methane emissions for the region in Europe between Orléans, Bremen, Białystok, and Garmisch-Partenkirchen are overestimated by the state-of-the-art inventories of the Emissions Database for Global Atmospheric Research (EDGAR) v4.2 FT2010 and the high-resolution emissions database developed by the Netherlands Organisation for Applied Scientific Research (TNO) as part of the Monitoring Atmospheric Composition and Climate project (TNO-MACC_III), possibly due to the disaggregation of emissions onto a spatial grid. Uncertainties in the carbon monoxide inventories used to compute the methaneemissionscontributetothediscrepancybetweenour inferred emissions and those from the inventories.

A portable Fourier transform spectrometer (FTS), model EM27/SUN, was deployed onboard the research vessel Polarstern to measure the column-average dry air mole fractions of carbon dioxide (XCO2) and methane (XCH4) by means of direct sunlight absorption spectrometry. We report on technical developments as well as data calibration and reduction measures required to achieve the targeted accuracy of fractions of a percent in retrieved XCO2 and XCH4 while operating the instrument under field conditions onboard the moving platform during a 6-week cruise on the Atlantic from Cape Town (South Africa, 34° S, 18° E; 5 March 2014) to Bremerhaven (Germany, 54° N, 19° E; 14 April 2014). We demonstrate that our solar tracker typically achieved a tracking precision of better than 0.05° toward the center of the sun throughout the ship cruise which facilitates accurate XCO2 and XCH4 retrievals even under harsh ambient wind conditions. We define several quality filters that screen spectra, e.g., when the field of view was partially obstructed by ship structures or when the lines-of-sight crossed the ship exhaust plume. The measurements in clean oceanic air, can be used to characterize a spurious air-mass dependency. After the campaign, deployment of the spectrometer alongside the TCCON (Total Carbon Column Observing Network) instrument at Karlsruhe, Germany, allowed for determining a calibration factor that makes the entire campaign record traceable to World Meteorological Organization (WMO) standards. Comparisons to observations of the GOSAT satellite and concentration fields modeled by the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) demonstrate that the observational setup is well suited to provide validation opportunities above the ocean and along interhemispheric transects.

We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 %) and a single-sounding precision of 13.4 ppb (∼ 0.74 %). The station-to-station bias (ameasure of the relative accuracy) is found to be 4.2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm−1 (∼ 0.30 %), single-sounding precision of 0.033 ppb ppm−1 (∼ 0.72 %)). The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r = 0.94–0.97), it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm) than any of the individual models whilst maintaining a small bias (0.15 ppm). This model median XCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N)) and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight. We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA). We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition to that already provided by the assimilated surface measurements.

We present lower/middle tropospheric column-averaged CH4 mole fraction time series measured by nine globally distributed ground-based FTIR (Fourier transform infrared) remote sensing experiments of the Network for the Detection of Atmospheric Composition Change (NDACC). We show that these data are well representative of the tropospheric regional-scale CH4 signal, largely independent of the local surface small-scale signals, and only weakly dependent on upper tropospheric/lower stratospheric (UTLS) CH4 variations. In order to achieve the weak dependency on the UTLS, we use an a posteriori correction method. We estimate a typical precision for daily mean values of about 0.5% and a systematic error of about 2.5%. The theoretical assessments are complemented by an extensive empirical study. For this purpose, we use surface in situ CH4 measurements made within the Global Atmosphere Watch (GAW) network and compare them to the remote sensing data. We briefly discuss different filter methods for removing the local small-scale signals from the surface in situ data sets in order to obtain the in situ regional-scale signals. We find good agreement between the filtered in situ and the remote sensing data. The agreement is consistent for a variety of timescales that are interesting for CH4 source/sink research: day-to-day, monthly, and inter-annual. The comparison study confirms our theoretical estimations and proves that the NDACC FTIR measurements can provide valuable data for investigating the cycle of CH4.

A new algorithm to retrieve nitrogen dioxide (NO2) column densities using MKIV ("Mark IV") Brewer spectrophotometers is described. The method includes several improvements, such as a more recent spectroscopic data set, the reduction of measurement noise, interference by other atmospheric species and instrumental settings, and a better determination of the zenith sky air mass factor. The technique was tested during an ad hoc calibration campaign at the high-altitude site of Izaña (Tenerife, Spain) and the results of the direct sun and zenith sky geometries were compared to those obtained by two reference instruments from the Network for the Detection of Atmospheric Composition Change (NDACC): a Fourier Transform Infrared Radiometer (FTIR) and an advanced visible spectrograph (RASAS-II) based on the differential optical absorption spectrometry (DOAS) technique. To determine the extraterrestrial constant, an easily implementable extension of the standard Langley technique for very clean sites without tropospheric NO2 was developed which takes into account the daytime linear drift of stratospheric nitrogen dioxide due to photochemistry. The measurement uncertainty was thoroughly determined by using a Monte Carlo technique. Poisson noise and wavelength misalignments were found to be the most influential contributors to the overall uncertainty, and possible solutions are proposed for future improvements. The new algorithm is backward-compatible, thus allowing for the reprocessing of historical data sets.

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 the previously reported theory that takes into account 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 have a range of independent data including 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 the a priori or in situ inversion, particularly over the tropics and the southern extratropics. Over the northern extratropics, 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 a 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 Amazonian fluxes that are reproduced over the 5 years we studied: a small peak from January to April and a larger peak from June to October, which are likely due to superimposed emissions from different geographical regions.

The project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) has shown that the sensor IASI aboard the satellite MetOp can measure the free tropospheric {H2O,δD} pair distribution twice per day on a quasi-global scale. Such data are very promising for investigating tropospheric moisture pathways, however, the complex data characteristics compromise their usage in the context of model evaluation studies. Here we present a tool that allows for simulating MUSICA MetOp/IASI {H2O,δD} pair remote sensing data for a given model atmosphere, thereby creating model data that have the remote sensing data characteristics assimilated. This model data can then be compared to the MUSICA data. The retrieval simulation method is based on the physical principles of radiative transfer and we show that the uncertainty of the simulations is within the uncertainty of the MUSICA MetOp/IASI products, i.e. the retrieval simulations are reliable enough. We demonstrate the working principle of the simulator by applying it to ECHAM5-wiso model data. The few case studies clearly reveal the large potential of the MUSICA MetOp/IASI {H2O,δD} data pairs for evaluating modelled moisture pathways. The tool is made freely available in form of MATLAB and Python routines and can be easily connected to any atmospheric water vapour isotopologue model.