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The Tropospheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel 5 Precursor (S5P) satellite, launched in October 2017, provides a wealth of atmospheric composition data, including total columns of carbon monoxide (TCCO) at high horizontal resolution (5.5 km × 7 km). Near-real-time TROPOMI TCCO data have been monitored in the global data assimilation system of the Copernicus Atmosphere Monitoring Service (CAMS) since November 2018 to assess the quality of the data. The CAMS system already routinely assimilates TCCO data from the Measurement of Pollution in the Troposphere (MOPITT) instrument and the Infrared Atmospheric Sounding Interferometer (IASI) outside the polar regions. The assimilation of TROPOMI TCCO data in the CAMS system was tested for the period 6 July to 31 December 2021, i.e. after the TROPOMI algorithm update to version 02.02.00 in July 2021. By assimilating TROPOMI TCCO observations, the CAMS CO columns increase by on average 8 %, resulting in an improved fit to independent observations (IAGOS aircraft profiles and NDACC Fourier transform infrared (FTIR) tropospheric and total-column CO data) compared to a version of the CAMS system where only TCCO from MOPITT and IASI is assimilated. The largest absolute and relative changes from the assimilation of TROPOMI CO are found in the lower and middle troposphere, i.e. that part of the atmosphere that is not already well constrained by the assimilated TIR MOPITT and IASI data. The largest impact near the surface comes from clear-sky TROPOMI data over land, and additional vertical information comes from the retrievals of measurements in cloudy conditions. July and August 2021 saw record numbers of boreal wildfires over North America and Russia, leading to large amounts of CO being released into the atmosphere. The paper assesses the impact of TROPOMI CO assimilation on selected CO plumes more closely. While the CO column can be well constrained by the assimilation of TROPOMI CO data, and the fit to individual IAGOS CO profiles in the lower and middle troposphere is considerably improved, the TROPOMI CO columns do not provide further constraints on individual plumes that are transported across continents and oceans at altitudes above 500 hPa.

In this study, we present a novel monitoring methodology that combines satellite retrievals and forecasts to detect local CH4 concentration anomalies worldwide. These anomalies are caused by rapidly changing anthropogenic emissions that significantly contribute to the CH4 atmospheric budget and by biases in the satellite retrieval data. The method uses high-resolution (7 km × 7 km) retrievals of total column CH4 from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite. Observations are combined with high-resolution CH4 forecasts (∼ 9 km) produced by the Copernicus Atmosphere Monitoring Service (CAMS) to provide departures (observations minus forecasts) at close to the satellite's native resolution at appropriate time. Investigating these departures is an effective way to link satellite measurements and emission inventory data in a quantitative manner. We perform filtering on the departures to remove the synoptic-scale and meso-alpha-scale biases in both forecasts and satellite observations. We then apply a simple classification scheme to the filtered departures to detect anomalies and plumes that are missing (e.g. pipeline or facility leaks), underreported or overreported (e.g. depleted drilling fields) in the CAMS emissions. The classification method also shows some limitations to detect emission anomalies only due to local satellite retrieval biases linked to albedo and scattering issues.

This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite provides measurements of carbon monoxide (CO) total column concentrations based on earthshine radiance measurements in the 2.3 µm spectral range with a spatial resolution of 7 km×7 km and daily global coverage. Due to the high accuracy of the observations, CO pollution can be detected over cities and industrial areas using single orbit overpasses. In this study, we analyzed local CO enhancements in an area around Iran from 1 November to 20 December 2017. We employed the Weather Research and Forecasting (WRF) model v3.8.1 using the EDGAR v4.2 emission inventory and evaluated CO emissions from the cities of Tehran, Yerevan, Urmia, and Tabriz on a spatial resolution comparable to that of TROPOMI. For background conditions, the WRF simulation agrees well with TROPOMI CO, with a mean difference of 5.7 %. However, the emissions for the city area had to be significantly increased in order to match the observations. Moreover, significant differences at the sub-city scale remain. To match the TROPOMI CO observations around the Armenian city of Yerevan, it is necessary to introduce CO emissions along a southeast arterial road of Yerevan. Overall, this hints at deficits in the EDGAR inventory in the region around Iran and indicates TROPOMI's capability to identify localized CO pollution on sub-city scales, which at the same time challenges current atmospheric modeling at high spatial and temporal resolution.

The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, has been measuring carbon monoxide (CO) concentrations in the Earth's atmosphere since early November 2017. In the first measurements, TROPOMI was able to measure CO concentrations of the high-pollution event in India of November 2017. In this paper, we studied the extent of the pollution in India, comparing the TROPOMI CO with modeled data from the Weather Research and Forecasting model (WRF) to identify the most important sources contributing to the high pollution, both at ground level and in the total column. We investigated the period 11–19 November 2017. We found that residential and commercial combustion was a much more important source of CO pollution than the post-monsoon crop burning during this period, which is in contrast to what media suggested and some studies on aerosol emissions found. Also, the high pollution was not limited to Delhi and its direct neighborhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavorable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed a buildup of CO during 11–14 November and a decline in CO after 15 November. The meteorological conditions, characterized by low wind speeds and shallow atmospheric boundary layers, were most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere. This emphasizes the important role of atmospheric dynamics in determining the air quality conditions at ground level and in the total column. Due to its rapidly growing population and economy, India is expected to encounter similar pollution events more often in future post-monsoon and winter seasons unless significant policy measures are taken to reduce residential and commercial emissions.

This study investigates the constraint provided by greenhouse gas measurements from space on surface fluxes. Imperfect knowledge of the light path through the atmosphere, arising from scattering by clouds and aerosols, can create biases in column measurements retrieved from space. To minimize the impact of such biases, ratios of total column retrieved CH4 and CO2 (Xratio) have been used. We apply the ratio inversion method described in Pandey et al. (2015) to retrievals from the Greenhouse Gases Observing SATellite (GOSAT). The ratio inversion method uses the measured Xratio as a weak constraint on CO2 fluxes. In contrast, the more common approach of inverting proxy CH4 retrievals (Frankenberg et al., 2005) prescribes atmospheric CO2 fields and optimizes only CH4 fluxes. The TM5–4DVAR (Tracer Transport Model version 5–variational data assimilation system) inverse modeling system is used to simultaneously optimize the fluxes of CH4 and CO2 for 2009 and 2010. The results are compared to proxy inversions using model-derived CO2 mixing ratios (XCO2model) from CarbonTracker and the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis CO2 product. The performance of the inverse models is evaluated using measurements from three aircraft measurement projects. Xratio and XCO2model are compared with TCCON retrievals to quantify the relative importance of errors in these components of the proxy XCH4 retrieval (XCH4proxy). We find that the retrieval errors in Xratio (mean = 0.61 %) are generally larger than the errors in XCO2model (mean = 0.24 and 0.01 % for CarbonTracker and MACC, respectively). On the annual timescale, the CH4 fluxes from the different satellite inversions are generally in agreement with each other, suggesting that errors in XCO2model do not limit the overall accuracy of the CH4 flux estimates. On the seasonal timescale, however, larger differences are found due to uncertainties in XCO2model, particularly over Australia and in the tropics. The ratio method stays closer to the a priori CH4 flux in these regions, because it is capable of simultaneously adjusting the CO2 fluxes. Over tropical South America, comparison to independent measurements shows that CO2 fields derived from the ratio method are less realistic than those used in the proxy method. However, the CH4 fluxes are more realistic, because the impact of unaccounted systematic uncertainties is more evenly distributed between CO2 and CH4. The ratio inversion estimates an enhanced CO2 release from tropical South America during the dry season of 2010, which is in accordance with the findings of Gatti et al. (2014) and Van der Laan et al. (2015). The performance of the ratio method is encouraging, because despite the added nonlinearity due to the assimilation of Xratio and the significant increase in the degree of freedom by optimizing CO2 fluxes, still consistent results are obtained with respect to other CH4 inversions.

We present a method for assimilating total column CH4 : CO2 ratio measurements from satellites for inverse modeling of CH4 and CO2 fluxes using the variational approach. Unlike conventional approaches, in which retrieved CH4 : CO2 are multiplied by model-derived total column CO2 and only the resulting CH4 is assimilated, our method assimilates the ratio of CH4 and CO2 directly and is therefore called the ratio method. It is a dual tracer inversion, in which surface fluxes of CH4 and CO2 are optimized simultaneously. The optimization of CO2 fluxes turns the hard constraint of prescribing model-derived CO2 fields into a weak constraint on CO2, which allows us to account for uncertainties in CO2. The method has been successfully tested in a synthetic inversion setup. We show that the ratio method is able to reproduce assumed true CH4 and CO2 fluxes starting from a prior, which is derived by perturbing the true fluxes randomly. We compare the performance of the ratio method with that of the traditional proxy approach and the use of only surface measurements for estimating CH4 fluxes. Our results confirm that the optimized CH4 fluxes are sensitive to the treatment of CO2, and that hard constraints on CO2 may significantly compromise results that are obtained for CH4. We find that the relative performance of ratio and proxy methods have a regional dependence. The ratio method performs better than the proxy method in regions where the CO2 fluxes are most uncertain. However, both ratio and proxy methods perform better than the surface-measurement-only inversion, confirming the potential of spaceborne measurements for accurately determining fluxes of CH4 and other greenhouse gases (GHGs).

This study investigates the use of total column CH4 (XCH4) retrievals from the SCIAMACHY satellite in- strument for quantifying large-scale emissions of methane. A unique data set from SCIAMACHY is available span- ning almost a decade of measurements, covering a period when the global CH4 growth rate showed a marked transition from stable to increasing mixing ratios. The TM5 4DVAR in- verse modelling system has been used to infer CH4 emissions from a combination of satellite and surface measurements for the period 2003–2010. In contrast to earlier inverse mod- elling studies, the SCIAMACHY retrievals have been cor- rected for systematic errors using the TCCON network of ground-based Fourier transform spectrometers. The aim is to further investigate the role of bias correction of satellite data in inversions. Methods for bias correction are discussed, and the sensitivity of the optimized emissions to alternative bias correction functions is quantified. It is found that the use of SCIAMACHY retrievals in TM5 4DVAR increases the esti- mated inter-annual variability of large-scale fluxes by 22 % compared with the use of only surface observations. The dif- ference in global methane emissions between 2-year periods before and after July 2006 is estimated at 27–35 Tg yr−1. The use of SCIAMACHY retrievals causes a shift in the emis- sions from the extra-tropics to the tropics of 50±25 Tg yr−1. The large uncertainty in this value arises from the uncertainty in the bias correction functions. Using measurements from the HIPPO and BARCA aircraft campaigns, we show that systematic errors in the SCIAMACHY measurements are a main factor limiting the performance of the inversions. To further constrain tropical emissions of methane using current and future satellite missions, extended validation capabilities in the tropics are of critical importance., JRC.H.2-Air and Climate

We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT). We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a) reduces the estimated global land sink of CO2, and (b) shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

This study presents a synthetic model intercomparison to investigate the importance of transport model errors for estimating the sources and sinks of CO2 using satellite measurements. The experiments were designed for testing the potential performance of the proposed CO2 lidar A-SCOPE, but also apply to other space borne missions that monitor total column CO2. The participating transport models IFS, LMDZ, TM3, and TM5 were run in forward and inverse mode using common a priori CO2 fluxes and initial concentrations. Forward simulations of column averaged CO2 (xCO2) mixing ratios vary between the models by σ=0.5 ppm over the continents and σ=0.27 ppm over the oceans. Despite the fact that the models agree on average on the sub-ppm level, these modest differences nevertheless lead to significant discrepancies in the inverted fluxes of 0.1 PgC/yr per 106 km2 over land and 0.03 PgC/yr per 106 km2 over the ocean. These transport model induced flux uncertainties exceed the target requirement that was formulated for the A-SCOPE mission of 0.02 PgC/yr per 106 km2, and could also limit the overall performance of other CO2 missions such as GOSAT. A variable, but overall encouraging agreement is found in comparison with FTS measurements at Park Falls, Darwin, Spitsbergen, and Bremen, although systematic differences are found exceeding the 0.5 ppm level. Because of this, our estimate of the impact of transport model uncerainty is likely to be conservative. It is concluded that to make use of the remote sensing technique for quantifying the sources and sinks of CO2 not only requires highly accurate satellite instruments, but also puts stringent requirements on the performance of atmospheric transport models. Improving the accuracy of these models should receive high priority, which calls for a closer collaboration between experts in atmospheric dynamics and tracer transport.

We present a new method to obtain accurate SCIAMACHY CO columns over clouded ocean scenes. Based on an improved version of the Iterative Maximum Likelihood Method (IMLM) retrieval algorithm, we now have retrieved five years of data over both land and clouded ocean scenes between 2003 and 2007. The ocean-cloud method uses the CH4 columns retrieved simultaneously with the CO columns to determine the cloud top height. The CH4 cloud top height is in good agreement with the FRESCO+ cloud top height determined from UV-VIS oxygen-A band measurements, providing confidence that the CH4 cloud top height is a good diagnostic of the cloud top height over (partially) clouded ocean scenes. The CO measurements over clouded ocean scenes have been compared with collocated modeled CO columns over the same clouds and agree well. Using clouded ocean scenes quadruples the number of useful CO measurements compared to land-only measurements. The five-year CO data set over land and clouded ocean scenes presented here is based on an improved version of the IMLM algorithm which includes a more accurate determination of the random instrument-noise error for CO. This leads to a smaller spread in the differences between single CO measurements and the corresponding model values. The new version, IMLM version 7.4, also uses updated spectroscopic parameters for H2O and CH4 but this has only a minor impact on the retrieved CO columns. The five-year data set shows significant interannual variability over land and over clouded ocean scenes. Three examples are highlighted: the Asian outflow of pollution over the northern Pacific, the biomass-burning outflow over the Indian Ocean originating from Indonesia, and biomass burning in Brazil. In general there is good agreement between observed and modeled seasonal cycles and interannual variability.

N2-broadened half widths and pressure shifts were obtained for transitions in the 2ν3 methane band. Laboratory measurements recorded at 0.011 cm−1 resolution with a Bruker 120 HR Fouriertransform spectrometer were analysed from 5860 to 6185 cm−1. A 140 cm gas cell was filled with methane at room temperature and N2 as foreign gas at pressures ranging from 125 to 900 hPa. A multispectrum nonlinear constrained least squares approach based on Optimal Estimation was applied to derive the spectroscopic parameters by simultaneously fitting laboratory spectra at different ambient pressures assuming a Voigt line-shape. At room temperature, the half widths ranged between 0.030 and 0.071 cm−1 atm−1, and the pressure shifts varied from –0.002 to –0.025 cm−1 atm−1 for transitions up to J"=10. Especially for higher rotational levels, we find systematically narrower lines than HITRAN predicts. The Q and R branch of the new set of spectroscopic parameters is further tested with ground based direct sun Fourier transform infrared (FTIR) measurements where systematic fit residuals reduce by about a factor of 3–4. We report the implication of those differences on atmospheric methane measurements using high-resolution ground based FTIR measurements as well as low-resolution spectra from the SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY (SCIAMACHY) instrument onboard ENVISAT. We find that for SCIAMACHY, a latitudinal and seasonally varying bias of about 1% can be introduced by erroneous broadening parameters.

A detailed sensitivity analysis of the Iterative Maximum Likelihood Method (IMLM) algorithm and its application to the SCIAMACHY 2.3 μm spectra is presented. The sensitivity analysis includes a detailed assessment of the impact of aerosols in the 2.3 μm range. Results show that near strong aerosol sources mineral dust and biomass aerosols can have an effect of ~7–10% on the CH4 total columns retrieved from this wavelength range if aerosol scattering is neglected in the retrieval algorithm. Similar but somewhat larger effects are found for CO, but due to the larger variability of CO these errors are less important. Away from strong sources much smaller effects of a few percent are found. Using CH4 as a proxy for CO and/or including aerosol information in the retrieval algorithm significantly reduces these errors for both CO and CH4. Spectroscopic uncertainties are mostly negligible except for uncertainties in the CH4 intrinsic line intensities, which can be important. Application of the IMLM algorithm to the SCIAMACHY 2.3 μm spectra shows that the quality of the retrieved CO and CH4 total columns is good, except for a bias for large instrument-noise errors which is partly due to remaining calibration issues. Polarization sensitivity of the SCIAMACHY instrument has a negligible effect on the retrieved CO and CH4 total columns. The H2O total columns, which have to be retrieved simultaneously with CO and CH4 due to overlapping absorption lines, agree well with H2O total columns from ECMWF data. This ensures that the fit to the H2O absorptions is of sufficient quality not to hamper the retrieved CO and CH4 total columns from SCIAMACHY spectra.

SCIAMACHY CO2 measurements show a large variability in total column CO2 over the Sahara desert of up to 10%, which is not anticipated from in situ measurements and cannot be explained by results of atmospheric models. Comparisons with colocated aerosol measurements by TOMS and MISR over the Sahara indicate that the seasonal variation of SCIAMACHY-observed CO2 strongly resembles seasonal variations of windblown dust. Correlation coefficients of monthly datasets of colocated MISR aerosol optical depth and SCIAMACHY CO2 vary between 0.6 and 0.8, indicating that about half of the CO2 variance is explained by aerosol optical depth. Radiative transfer model calculations confirm the role of dust and can explain the size of the errors. Sensitivity tests suggest that the remaining variance may largely be explained by variations in the vertical distribution of dust. Further calculations for a few typical aerosol classes and a broad range of atmospheric conditions show that the impact of aerosols on SCIAMACHY retrieved CO2 is by far the largest over the Sahara, but may also reach significant levels elsewhere. Over the continents, aerosols lead mostly to overestimated CO2 columns with the exception of biomass burning plumes and dark coniferous forests. Inverse modelling calculations confirm that aerosol correction of SCIAMACHY CO2 measurements is needed to derive meaningful source and sink estimates. Methods for correcting aerosol-induced errors exist, but so far mainly on the basis of theoretical considerations. As demonstrated by this study, SCIAMACHY may contribute to a verification of such methods using real data.

The near-infrared spectra measured with the SCIAMACHY instrument on board the ENVISAT satellite suffer from several instrument calibration problems. The effects of three important instrument calibration issues on the retrieved methane (CH4) and carbon monoxide (CO) total columns have been investigated: the effects of the growing ice layer on the near-infrared detectors, the effects of the orbital variation of the instrument dark signal, and the effects of the dead/bad detector pixels. Corrections for each of these instrument calibration issues have been defined. The retrieved CH4 and CO total columns including these corrections show good agreement with CO measurements from the MOPITT satellite instrument and with CH4 model calculations by the chemistry transport model TM3. Using a systematic approach, it is shown that all three instrument calibration issues have a significant effect on the retrieved CH4 and CO total columns. However, the impact on the CH4 total columns is more pronounced than for CO, because of its smaller variability. Results for three different wavelength ranges are compared and show good agreement. The growing ice layer and the orbital variation of the dark signal show a systematic, but time-dependent effect on the retrieved CH4 and CO total columns, whereas the effect of the dead/bad pixels is rather unpredictable: some dead pixels show a random effect, some more systematic, and others no effect at all. The importance of accurate corrections for each of these instrument calibration issues is illustrated using examples where inaccurate corrections lead to a wrong interpretation of the results.

International audience; SCIAMACHY on ENVISAT allows measurement of different trace gases including those most abundant in the troposphere (e.g. CO2, NO2, CH4, BrO, SO2). However, clouds in the observed scenes can severely hinder the observation of tropospheric gases. Several cloud detection algorithms have been developed for GOME on ERS-2 which can be applied to SCIAMACHY. The GOME cloud algorithms, however, suffer from the inadequacy of not being able to distinguish between clouds and ice/snow covered surfaces because GOME only covers the UV, VIS and part of the NIR wavelength range (240-790 nm). As a result these areas are always flagged as clouded, and therefore often not used. Here a method is presented which uses the SCIAMACHY measurements in the wavelength range between 450 nm and 1.6 µm to make a distinction between clouds and ice/snow covered surfaces. The algorithm is developed using collocated MODIS observations. The algorithm presented here is specifically developed to identify cloud-free SCIAMACHY observations. The SCIAMACHY Polarisation Measurement Devices (PMDs) are used for this purpose because they provide higher spatial resolution compared to the main spectrometer measurements.

S. Houweling1,2, M. Krol1,2,3, P. Bergamaschi4, C. Frankenberg5, E. J. Dlugokencky6, I. Morino7, J. Notholt8, V. Sherlock9, D. Wunch10, V. Beck11, C. Gerbig11, H. Chen12,13, E. A. Kort14, T. Rockmann2, and I. Aben1 1SRON Netherlands Institute for Space Research, Utrecht, the Netherlands 2Institute for Marine and Atmospheric Research (IMAU), Utrecht University, Utrecht, the Netherlands 3Department of Meteorology and Air Quality (MAQ), Wageningen University and Research Centre, Wageningen, the Netherlands 4European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra (Va), Italy 5Jet Propulsion Laboratory, Pasadena, CA, USA 6NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO, USA 7Center for Global Environmental Research, National Institute for Environmental Studies (NIES) Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan 8Institute of Environmental Physics, University of Bremen, Bremen, Germany 9National Institute of Water and Atmospheric Research (NIWA), P.O. Box 14-901, Wellington, New Zealand 10Caltech, Pasadena, CA, USA 11Max Planck Institute for Biogeochemistry, Jena, Germany 12Center for Isotope Research (CIO), University of Groningen, the Netherlands 13CIRES, University of Colorado, Boulder, CO, USA 14Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA

We apply a four-dimensional variational (4DVAR) data assimilation system to optimize carbon monoxide (CO) emissions for 2003 and 2004 and to reduce the uncertainty of emission estimates from individual sources using the chemistry transport model TM5. The system is designed to assimilate large (satellite) datasets, but in the current study only a limited amount of surface network observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Monitoring Division (GMD) is used to test the 4D-VAR system. By design, the system is capable to adjust the emissions in such a way that the posterior simulation reproduces background CO mixing ratios and large-scale pollution events at background stations. Uncertainty reduction up to 60% in yearly emissions is observed over well-constrained regions and the inferred emissions compare well with recent studies for 2004. However, with the limited amount of data from the surface network, the system becomes data sparse resulting in a large solution space. Sensitivity studies have shown that model uncertainties (e.g., vertical distribution of biomass burning emissions and the OH field) and the prior inventories used, influence the inferred emission estimates. Also, since the observations only constrain total CO emissions, the 4D-VAR system has difficulties in separating anthropogenic and biogenic sources in particular. The inferred emissions are validated with NOAA aircraft data over North America and the agreement is significantly improved from the prior to posterior simulation. Validation with the Measurements Of Pollution In The Troposphere (MOPITT) instrument version 4 (V4) shows a slight improved agreement over the wellconstrained Northern Hemisphere and in the tropics (except for the African continent). However, the model simulation with posterior emissions underestimates MOPITT CO total columns on the remote Southern Hemisphere (SH) by about 10 %. This is caused by a reduction in SH CO sources mainly due to surface stations on the high southern latitudes., JRC.H.2-Air and Climate