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Methane is an important atmospheric greenhouse gas and an adequate understanding of its emission sources is needed for climate change assessments, predictions, and the development and verification of emission mitigation strategies. Satellite retrievals of near-surface-sensitive column-averaged dry-air mole fractions of atmospheric methane, i.e. XCH4, can be used to quantify methane emissions. Maps of time-averaged satellite-derived XCH4 show regionally elevated methane over several methane source regions. In order to obtain methane emissions of these source regions we use a simple and fast data-driven method to estimate annual methane emissions and corresponding 1σ uncertainties directly from maps of annually averaged satellite XCH4. From theoretical considerations we expect that our method tends to underestimate emissions. When applying our method to high-resolution atmospheric methane simulations, we typically find agreement within the uncertainty range of our method (often 100 %) but also find that our method tends to underestimate emissions by typically about 40 %. To what extent these findings are model dependent needs to be assessed. We apply our method to an ensemble of satellite XCH4 data products consisting of two products from SCIAMACHY/ENVISAT and two products from TANSO-FTS/GOSAT covering the time period 2003–2014. We obtain annual emissions of four source areas: Four Corners in the south-western USA, the southern part of Central Valley, California, Azerbaijan, and Turkmenistan. We find that our estimated emissions are in good agreement with independently derived estimates for Four Corners and Azerbaijan. For the Central Valley and Turkmenistan our estimated annual emissions are higher compared to the EDGAR v4.2 anthropogenic emission inventory. For Turkmenistan we find on average about 50 % higher emissions with our annual emission uncertainty estimates overlapping with the EDGAR emissions. For the region around Bakersfield in the Central Valley we find a factor of 5–8 higher emissions compared to EDGAR, albeit with large uncertainty. Major methane emission sources in this region are oil/gas and livestock. Our findings corroborate recently published studies based on aircraft and satellite measurements and new bottom-up estimates reporting significantly underestimated methane emissions of oil/gas and/or livestock in this area in EDGAR.

© 2018 The Author(s). Published by IOP Publishing Ltd. Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO2 (ffCO2) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO2 by measuring radiocarbon (14C) in CO2, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014-15. There is strong agreement between the measurements and ffCO2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO2 emissions are consistent with the California Air Resources Board's reported ffCO2 emissions, providing tentative validation of California's reported ffCO2 emissions in 2014-15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Analysis systems incorporating atmospheric observations could provide a powerful tool for validating fossil fuel CO2 (ffCO2) emissions reported for individual regions, provided that fossil fuel sources can be separated from other CO2 sources or sinks and atmospheric transport can be accurately accounted for. We quantified ffCO2 by measuring radiocarbon (14C) in CO2, an accurate fossil-carbon tracer, at nine observation sites in California for three months in 2014–15. There is strong agreement between the measurements and ffCO2 simulated using a high-resolution atmospheric model and a spatiotemporally-resolved fossil fuel flux estimate. Inverse estimates of total in-state ffCO2 emissions are consistent with the California Air Resources Board's reported ffCO2 emissions, providing tentative validation of California's reported ffCO2 emissions in 2014–15. Continuing this prototype analysis system could provide critical independent evaluation of reported ffCO2 emissions and emissions reductions in California, and the system could be expanded to other, more data-poor regions.

Thermal infrared (IR) radiances measured near 8 microns contain information about the vertical distribution of water vapor (H2O), the water isotopologue HDO, and methane (CH4), key gases in the water and carbon cycles. Previous versions (Version 4 or less) of the TES profile retrieval algorithm used a "spectral-window" approach to minimize uncertainty from interfering species at the expense of reduced vertical resolution and sensitivity. In this manuscript we document changes to the vertical resolution and uncertainties of the TES version 5 retrieval algorithm. In this version (Version 5), joint estimates of H2O, HDO, CH4 and nitrous oxide (N2O) are made using radiances from almost the entire spectral region between 1100 cm−1 and 1330 cm−1. The TES retrieval constraints are also modified in order to better use this information. The new H2O estimates show improved vertical resolution in the lower troposphere and boundary layer, while the new HDO/H2O estimates can now profile the HDO/H2O ratio between 925 hPa and 450 hPa in the tropics and during summertime at high latitudes. The new retrievals are now sensitive to methane in the free troposphere between 800 and 150 mb with peak sensitivity near 500 hPa; whereas in previous versions the sensitivity peaked at 200 hPa. However, the upper troposphere methane concentrations are biased high relative to the lower troposphere by approximately 4% on average. This bias is likely related to temperature, calibration, and/or methane spectroscopy errors. This bias can be mitigated by normalizing the CH4 estimate by the ratio of the N2O estimate relative to the N2O prior, under the assumption that the same systematic error affects both the N2O and CH4 estimates. We demonstrate that applying this ratio theoretically reduces the CH4 estimate for non-retrieved parameters that jointly affect both the N2O and CH4 estimates. The relative upper troposphere to lower troposphere bias is approximately 2.8% after this bias correction. Quality flags based upon the vertical variability of the methane and N2O estimates can be used to reduce this bias further. While these new CH4, HDO/H2O, and H2O estimates are consistent with previous TES retrievals in the altitude regions where the sensitivities overlap, future comparisons with independent profile measurement will be required to characterize the biases of these new retrievals and determine if the calculated uncertainties using the new constraints are consistent with actual uncertainties.

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).

With the advent of dedicated greenhouse-gas space-borne spectrometers sporting high resolution spectra in the O₂ A-band spectral region (755-774 nm), the retrieval of chlorophyll fluorescence has become feasible on a global scale. If unaccounted for, however, fluorescence can indirectly perturb the greenhouse gas retrievals as it perturbs the oxygen absorption features. As atmospheric CO₂ measurements are used to invert net fluxes at the land-atmosphere interface, a bias caused by fluorescence can be crucial as it will spatially correlate with the fluxes to be inverted. Avoiding a bias and retrieving fluorescence accurately will provide additional constraints on both the net and gross fluxes in the global carbon cycle. We show that chlorophyll fluorescence, if neglected, systematically interferes with full-physics multi-band XCO₂ retrievals using the O₂ A-band. Systematic biases in XCO₂ can amount to + 1 ppm if fluorescence constitutes 1% to the continuum level radiance. We show that this bias can be largely eliminated by simultaneously fitting fluorescence in a full-physics based retrieval.If fluorescence is the primary target, a dedicated but very simple retrieval based purely on Fraunhofer lines is shown to be more accurate and very robust even in the presence of large scattering optical depths. We find that about 80% of the surface fluorescence is retained at the top-of-atmosphere even for cloud optical thicknesses around 2-5. We further show that small instrument modifications to future O₂ A-band spectrometer spectral ranges can result in largely reduced random errors in chlorophyll fluorescence, paving the way towards a more dedicated instrument exploiting solar absorption features only.

資料番号: PA1110063000

資料番号: PA1110076000

At the beginning of 2009 new space-borne observations of dry-air column-averaged mole fractions of atmospheric methane (XCH$_{4}$) became available from the Thermal And Near infrared Sensor for carbon Observations–Fourier Transform Spectrometer (TANSO-FTS) instrument on board the Greenhouse Gases Observing SATellite (GOSAT). Until April 2012 concurrent methane (CH$_{4}$) retrievals were provided by the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) instrument on board the ENVironmental SATellite (ENVISAT). The GOSAT and SCIAMACHY XCH$_{4}$ retrievals can be compared during the period of overlap. We estimate monthly average CH$_{4}$ emissions between January 2010 and December 2011, using the TM5-4DVAR inverse modelling system. In addition to satellite data, high-accuracy measurements from the Cooperative Air Sampling Network of the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL) are used, providing strong constraints on the remote surface atmosphere. We discuss five inversion scenarios that make use of different GOSAT and SCIAMACHY XCH$_{4}$ retrieval products, including two sets of GOSAT proxy retrievals processed independently by the Netherlands Institute for Space Research (SRON)/Karlsruhe Institute of Technology (KIT), and the University of Leicester (UL), and the RemoTeC “Full- Physics” (FP) XCH$_{4}$ retrievals available from SRON/KIT. The GOSAT-based inversions show significant reductions in the root mean square (rms) difference between retrieved and modelled XCH$_{4}$, and require much smaller bias corrections compared to the inversion using SCIAMACHY retrievals, reflecting the higher precision and relative accuracy of the GOSAT XCH$_{4}$. Despite the large differences between the GOSAT and SCIAMACHY retrievals, 2-year average emission maps show overall good agreement among all satellitebased inversions, with consistent flux adjustment patterns, particularly across equatorial Africa and North America. Over North America, the satellite inversions result in a significant redistribution of CH$_{4}$ emissions from North-East to South-Central United States. This result is consistent with recent independent studies suggesting a systematic underestimation of CH$_{4}$ emissions from North American fossil fuel sources in bottom-up inventories, likely related to natural gas production facilities. Furthermore, all four satellite inversions yield lower CH$_{4}$ fluxes across the Congo basin compared to the NOAA-only scenario, but higher emissions across tropical East Africa. The GOSAT and SCIAMACHY inversions show similar performance when validated against independent shipboard and aircraft observations, and XCH$_{4}$ retrievals available from the Total Carbon Column Observing Network (TCCON).

Measurements of the atmospheric HDO/H$_{2}$O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H$_{2}$O ratio data set from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The data set is extended with 2 additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier transform spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap among the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of -35±30‰ compared to TCCON and -69±15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (~-60 to -80 ‰) at the highest latitudes and smallest (~-20 to -30 ‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H$_{2}$O columns. This correction leads to a humidity- and latitude-dependent shift in δD and an improvement of the bias by 27 ‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ~115 ‰, which is reduced by a factor ~2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapour and shows the added information that HDO/H$_{2}$O measurements provide when used in combination with variations in humidity.

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO₂, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH₄, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH₄ precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

We apply a cloud slicing technique (CST), originally developed for Total Ozone Mapping Spectrometer (TOMS) ozone observations, to CO vertical column densities retrieved from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). CST makes use of the shielding effect of clouds and combines trace gas column measurements of cloudy pixels with different cloud heights to retrieve fractional columns aloft. Here we determine seasonal mean tropospheric CO profiles at a vertical resolution of about 1 km, which is much finer than what can be obtained from thermal infrared (IR) instruments. However, since both the atmospheric CO profiles and the effective cloud heights depend systematically on meteorology, and in addition part of the retrieved signal originates from the clear part of the satellite ground pixel, the profiles retrieved from the CST have to be interpreted with care. We compare the seasonal mean SCIAMACHY CO profiles with the output from two atmospheric models sampled in the same way as the satellite observations. We find in general good agreement of the spatial patterns, but systematic differences in the absolute values are observed in both hemispheres (more strongly in the Northern Hemisphere), indicating that the source strengths in the emission inventories are probably underestimated.

Column-averaged dry-air mole fractions of carbon dioxide and methane have been retrieved from spectra acquired by the TANSO-FTS (Thermal And Near-infrared Sensor for carbon Observations-Fourier Transform Spectrometer) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Cartography) instruments on board GOSAT (Greenhouse gases Observing SATellite) and ENVISAT (ENVIronmental SATellite), respectively, using a range of European retrieval algorithms. These retrievals have been compared with data from ground-based high-resolution Fourier transform spectrometers (FTSs) from the Total Carbon Column Observing Network (TCCON). The participating algorithms are the weighting function modified differential optical absorption spectroscopy (DOAS) algorithm (WFMD, University of Bremen), the Bremen optimal estimation DOAS algorithm (BESD, University of Bremen), the iterative maximum a posteriori DOAS (IMAP, Jet Propulsion Laboratory (JPL) and Netherlands Institute for Space Research algorithm (SRON)), the proxy and full-physics versions of SRON's RemoTeC algorithm (SRPR and SRFP, respectively) and the proxy and full-physics versions of the University of Leicester's adaptation of the OCO (Orbiting Carbon Observatory) algorithm (OCPR and OCFP, respectively). The goal of this algorithm inter-comparison was to identify strengths and weaknesses of the various so-called round- robin data sets generated with the various algorithms so as to determine which of the competing algorithms would proceed to the next round of the European Space Agency's (ESA) Greenhouse Gas Climate Change Initiative (GHG-CCI) project, which is the generation of the so-called Climate Research Data Package (CRDP), which is the first version of the Essential Climate Variable (ECV) "greenhouse gases" (GHGs). For XCO2, all algorithms reach the precision requirements for inverse modelling (< 8 ppm), with only WFMD having a lower precision (4.7 ppm) than the other algorithm products (2.4–2.5 ppm). When looking at the seasonal relative accuracy (SRA, variability of the bias in space and time), none of the algorithms have reached the demanding < 0.5 ppm threshold. For XCH4, the precision for both SCIAMACHY products (50.2 ppb for IMAP and 76.4 ppb for WFMD) fails to meet the < 34 ppb threshold for inverse modelling, but note that this work focusses on the period after the 2005 SCIAMACHY detector degradation. The GOSAT XCH4 precision ranges between 18.1 and 14.0 ppb. Looking at the SRA, all GOSAT algorithm products reach the < 10 ppm threshold (values ranging between 5.4 and 6.2 ppb). For SCIAMACHY, IMAP and WFMD have a SRA of 17.2 and 10.5 ppb, respectively.

The relative abundance of the heavy water isotopologue HDO provides a deeper insight into the atmospheric hydrological cycle. The SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) allows for global retrievals of the ratio HDO/HO in the 2.3 micron wavelength range. However, the spectroscopy of water lines in this region remains a large source of uncertainty for these retrievals. We therefore evaluate and improve the water spectroscopy in the range 4174–4300 cm⁻¹ and test if this reduces systematic uncertainties in the SCIAMACHY retrievals of HDO/H₂O. We use a laboratory spectrum of water vapour to fit line intensity, air broadening and wavelength shift parameters. The improved spectroscopy is tested on a series of ground-based high resolution FTS spectra as well as on SCIAMACHY retrievals of H2O and the ratio HDO/H₂O. We find that the improved spectroscopy leads to lower residuals in the FTS spectra compared to HITRAN 2008 and Jenouvrier et al. (2007) spectroscopy, and the retrievals become more robust against changes in the retrieval window. For both the FTS and SCIAMACHY measurements, the retrieved total H₂O columns decrease by 2–4% and we find a negative shift of the HDO/H₂O ratio, which for SCIAMACHY is partly compensated by changes in the retrieval setup and calibration software. The updated SCIAMACHY HDO/H₂O product shows somewhat steeper latitudinal and temporal gradients and a steeper Rayleigh distillation curve, strengthening previous conclusions that current isotope-enabled general circulation models underestimate the variability in the near-surface HDO/H₂O ratio.

Monitoring the global distribution and long-term variations of CO2 sources and sinks is required for characterizing the global carbon budget. Total column measurements are useful for estimating regional-scale fluxes; however, model transport remains a significant error source, particularly for quantifying local sources and sinks. To improve the capability of estimating regional fluxes, we estimate lower tropospheric CO2 concentrations from ground-based near-infrared (NIR) measurements with space-based thermal infrared (TIR) measurements. The NIR measurements are obtained from the Total Carbon Column Observing Network (TCCON) of solar measurements, which provide an estimate of the total CO2 column amount. Estimates of tropospheric CO2 that are co-located with TCCON are obtained by assimilating Tropospheric Emission Spectrometer (TES) free tropospheric CO2 estimates into the GEOS-Chem model. We find that quantifying lower tropospheric CO2 by subtracting free tropospheric CO2 estimates from total column estimates is a linear problem, because the calculated random uncertainties in total column and lower tropospheric estimates are consistent with actual uncertainties as compared to aircraft data. For the total column estimates, the random uncertainty is about 0.55 ppm with a bias of −5.66 ppm, consistent with previously published results. After accounting for the total column bias, the bias in the lower tropospheric CO2 estimates is 0.26 ppm with a precision (one standard deviation) of 1.02 ppm. This precision is sufficient for capturing the winter to summer variability of approximately 12 ppm in the lower troposphere; double the variability of the total column. This work shows that a combination of NIR and TIR measurements can profile CO2 with the precision and accuracy needed to quantify lower tropospheric CO2 variability.

The causes of renewed growth in the atmospheric CH4 burden since 2007 are still poorly understood and subject of intensive scientific discussion. We present a reanalysis of global CH4 emissions during the 2000s, based on the TM5-4DVAR inverse modeling system. The model is optimized using high-accuracy surface observations from NOAA ESRL's global air sampling network for 2000-2010 combined with retrievals of column-averaged CH4 mole fractions from SCIAMACHY onboard ENVISAT (starting 2003).Using climatological OH fields, derived global total emissions for 2007-2010 are 16-20 Tg CH 4/yr higher compared to 2003-2005. Most of the inferred emission increase was located in the tropics (9-14 Tg CH4/yr) and mid-latitudes of the northern hemisphere (6-8 Tg CH4/yr), while no significant trend was derived for Arctic latitudes. The atmospheric increase can be attributed mainly to increased anthropogenic emissions, but the derived trend is significantly smaller than estimated in the EDGARv4.2 emission inventory. Superimposed on the increasing trend in anthropogenic CH4 emissions are significant inter-annual variations (IAV) of emissions from wetlands (up to ±10 Tg CH4/yr), and biomass burning (up to ±7 Tg CH4/yr). Sensitivity experiments, which investigated the impact of the SCIAMACHY observations (versus inversions using only surface observations), of the OH fields used, and of a priori emission inventories, resulted in differences in the detailed latitudinal attribution of CH4 emissions, but the IAV and trends aggregated over larger latitude bands were reasonably robust. All sensitivity experiments show similar performance against independent shipboard and airborne observations used for validation, except over Amazonia where satellite retrievals improved agreement with observations in the free troposphere. Key Points A reanalysis of global CH4 emissions during the 2000s is presented derived global total emissions 2007-2010 16-20 Tg CH4/yr higher than 2003-2005 increase mainly in the tropics and NH mid-latitudes ©2013. American Geophysical Union. All Rights Reserved.

Over the past decade the development of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) retrievals has increased the interest in the use of satellite measurements for studying the global sources and sinks of methane. Meanwhile, measurements are becoming available from the more advanced Greenhouse Gases Observing Satellite (GOSAT). The aim of this study is to investigate the application of GOSAT retrievals to inverse modeling, for which we make use of the TM5-4DVAR inverse modeling framework. Inverse modeling calculations are performed using data from two different retrieval approaches: a full physics and a lightpath proxy ratio method. The performance of these inversions is analyzed in comparison with inversions using SCIAMACHY retrievals and measurements from the National Oceanic and Atmospheric Administration-Earth System Research Laboratory flask-sampling network. In addition, we compare the inversion results against independent surface, aircraft, and total-column measurements. Inversions with GOSAT data show good agreement with surface measurements, whereas for SCIAMACHY a similar performance can only be achieved after significant bias corrections. Some inconsistencies between surface and total-column methane remain in the Southern Hemisphere. However, comparisons with measurements from the Total Column Carbon Observing Network in situ Fourier transform spectrometer network indicate that those may be caused by systematic model errors rather than by shortcomings in the GOSAT retrievals. The global patterns of methane emissions derived from SCIAMACHY (with bias correction) and GOSAT retrievals are in remarkable agreement and allow an increased resolution of tropical emissions. The satellite inversions increase tropical methane emission by 30 to 60 TgCH4/yr compared to initial a priori estimates, partly counterbalanced by reductions in emissions at midlatitudes to high latitudes.

International audience; Climate models suggest an important role for land-atmosphere feedbacks on climate, but exhibit a large dispersion in the simulation of this role. We focus here on the role of continental recycling in the intraseasonal variability of continental moisture, and we explore the possibility of using water isotopic measurements to observationally constrain this role. Based on water tagging, we design a diagnostic, named D1, to estimate the role of continental recycling on the intraseasonal variability of continental moisture simulated by the general circulation model LMDZ. In coastal regions, the intraseasonal variability of continental moisture is mainly driven by the variability in oceanic moisture convergence. More inland, the role of continental recycling becomes important. The simulation of this role is sensitive to model parameters modulating evapotranspiration. Then we show that dD in the low-level water vapor is a good tracer for continental recycling, due to the enriched signature of transpiration. Over tropical land regions, the intraseasonal relationship between dD and precipitable water, named D1-iso, is a good observational proxy for D1. We test the possibility of using D1-iso for model evaluation using two satellite data sets: GOSAT and TES. LMDZ captures well the spatial patterns of D1-iso, but underestimates its values. However, a more accurate description of how atmospheric processes affect the isotopic composition of water vapor is necessary before concluding with certitude that LMDZ underestimates the role of continental recycling. © 2013. American Geophysical Union. All Rights Reserved.

International audience; Over the past decade the development of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) retrievals has increased the interest in the use of satellite measurements for studying the global sources and sinks of methane. Meanwhile, measurements are becoming available from the more advanced Greenhouse Gases Observing Satellite (GOSAT). The aim of this study is to investigate the application of GOSAT retrievals to inverse modeling, for which we make use of the TM5-4DVAR inverse modeling framework. Inverse modeling calculations are performed using data from two different retrieval approaches: a full physics and a lightpath proxy ratio method. The performance of these inversions is analyzed in comparison with inversions using SCIAMACHY retrievals and measurements from the National Oceanic and Atmospheric Administration-Earth System Research Laboratory flask-sampling network. In addition, we compare the inversion results against independent surface, aircraft, and total-column measurements. Inversions with GOSAT data show good agreement with surface measurements, whereas for SCIAMACHY a similar performance can only be achieved after significant bias corrections. Some inconsistencies between surface and total-column methane remain in the Southern Hemisphere. However, comparisons with measurements from the Total Column Carbon Observing Network in situ Fourier transform spectrometer network indicate that those may be caused by systematic model errors rather than by shortcomings in the GOSAT retrievals. The global patterns of methane emissions derived from SCIAMACHY (with bias correction) and GOSAT retrievals are in remarkable agreement and allow an increased resolution of tropical emissions. The satellite inversions increase tropical methane emission by 30 to 60 TgCH4/yr compared to initial a priori estimates, partly counterbalanced by reductions in emissions at midlatitudes to high latitudes. Key Points GOSAT and SCIAMACHY retrievals lead to comparable emission patterns GOSAT retrievals are found much less affected by biases than SCIAMACHY Combining satellite and in-situ observations point to remaining inconsistencies ©2013. American Geophysical Union. All Rights Reserved.

Here, we report preliminary estimates of the column averaged carbon dioxide (CO_2) dry air mole fraction, X_(CO_2), retrieved from spectra recorded over land by the Greenhouse gases Observing Satellite, GOSAT (nicknamed "Ibuki"), using retrieval methods originally developed for the NASA Orbiting Carbon Observatory (OCO) mission. After screening for clouds and other known error sources, these retrievals reproduce much of the expected structure in the global X_(CO_2) field, including its variation with latitude and season. However, low yields of retrieved X_(CO_2) over persistently cloudy areas and ice covered surfaces at high latitudes limit the coverage of some geographic regions, even on seasonal time scales. Comparisons of early GOSAT X_(CO_2) retrievals with X_(CO_2) estimates from the Total Carbon Column Observing Network (TCCON) revealed a global, −2% (7–8 parts per million, ppm, with respect to dry air) X_(CO_2) bias and 2 to 3 times more variance in the GOSAT retrievals. About half of the global X_(CO_2) bias is associated with a systematic, 1% overestimate in the retrieved air mass, first identified as a global +10 hPa bias in the retrieved surface pressure. This error has been attributed to errors in the O_2 A-band absorption cross sections. Much of the remaining bias and spurious variance in the GOSAT X_(CO_2) retrievals has been traced to uncertainties in the instrument's calibration, oversimplified methods for generating O_2 and CO_2 absorption cross sections, and other subtle errors in the implementation of the retrieval algorithm. Many of these deficiencies have been addressed in the most recent version (Build 2.9) of the retrieval algorithm, which produces negligible bias in X_(CO_2) on global scales as well as a ~30% reduction in variance. Comparisons with TCCON measurements indicate that regional scale biases remain, but these could be reduced by applying empirical corrections like those described by Wunch et al. (2011b). We recommend that such corrections be applied before these data are used in source sink inversion studies to minimize spurious fluxes associated with known biases. These and other lessons learned from the analysis of GOSAT data are expected to accelerate the delivery of high quality data products from the Orbiting Carbon Observatory-2 (OCO-2), once that satellite is successfully launched and inserted into orbit.

This work describes the NASA Atmospheric CO_2 Observations from Space (ACOS) X_(CO_2) retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

Tropical regions, especially the Amazon region, account for large emissions of methane (CH4). Here, we present CH4 observations from two airborne campaigns conducted within the BARCA (Balanço Atmosférico Regional de Carbono na Amazônia) project in the Amazon basin in November 2008 (end of the dry season) and May 2009 (end of the wet season). We performed continuous measurements of CH4 onboard an aircraft for the first time in the Amazon region, covering the whole Amazon basin with over 150 vertical profiles between altitudes of 500 m and 4000 m. The observations support the finding of previous ground-based, airborne, and satellite measurements that the Amazon basin is a large source of atmospheric CH4. Isotope analysis verified that the majority of emissions can be attributed to CH4 emissions from wetlands, while urban CH4 emissions could be also traced back to biogenic origin. A comparison of five TM5 based global CH4 inversions with the observations clearly indicates that the inversions using SCIAMACHY observations represent the BARCA observations best. The calculated CH4 flux estimate obtained from the mismatch between observations and TM5-modeled CH4 fields ranges from 36 to 43 mg m−2 d−1 for the Amazon lowland region.

We compare two conceptually different methods for determining methane column-averaged mixing ratios (XCH4) from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared (SWIR) measurements. These methods account differently for light scattering by aerosol and cirrus. The proxy method retrieves a CO2 column which, in conjunction with prior knowledge on CO2 acts as a proxy for scattering effects. The physics-based method accounts for scattering by retrieving three effective parameters of a scattering layer. Both retrievals are validated on a 19-month data set using ground-based XCH4 measurements at 12 stations of the Total Carbon Column Observing Network (TCCON), showing comparable performance: for the proxy retrieval we find station-dependent retrieval biases from -0.312% to 0.421% of XCH4 with a standard deviation of 0.22% and a typical precision of 17 ppb. The physics method shows biases between -0.836% and -0.081% with a standard deviation of 0.24% and a precision similar to the proxy method. Complementing this validation we compared both retrievals with simulated methane fields from a global chemistry-transport model. This identified shortcomings of both retrievals causing biases of up to 1ings and provide a satisfying validation of any methane retrieval from space-borne SWIR measurements, in our opinion it is essential to further expand the network of TCCON stations. © Copyright 2012 by the American Geophysical Union.

With the advent of dedicated greenhouse gas space-borne spectrometers sporting high resolution spectra in the O2 A-band spectral region (755–774 nm), the retrieval of chlorophyll fluorescence has become feasible on a global scale. If unaccounted for, however, fluorescence can indirectly perturb the greenhouse gas retrievals as it perturbs the oxygen absorption features. As atmospheric CO2 measurements are used to invert net fluxes at the land–atmosphere interface, a bias caused by fluorescence can be crucial as it will spatially correlate with the fluxes to be inverted. Avoiding a bias and retrieving fluorescence accurately will provide additional constraints on both the net and gross fluxes in the global carbon cycle. We show that chlorophyll fluorescence, if neglected, systematically interferes with full-physics multi-band XCO2 retrievals using the O2 A-band. Systematic biases in XCO2 can amount to +1 ppm if fluorescence constitutes 1% to the continuum level radiance. We show that this bias can be largely eliminated by simultaneously fitting fluorescence in a full-physics based retrieval. If fluorescence is the primary target, a dedicated but very simple retrieval based purely on Fraunhofer lines is shown to be more accurate and very robust even in the presence of large scattering optical depths. We find that about 80% of the surface fluorescence is retained at the top-of-atmosphere, even for cloud optical thicknesses around 2–5. We further show that small instrument modifications to future O2 A-band spectrometer spectral ranges can result in largely reduced random errors in chlorophyll fluorescence, paving the way towards a more dedicated instrument exploiting solar absorption features only.

We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO_2 (X_(CO_2)) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO_2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X_(CO_2) south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and X_(CO_2) in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

We performed an intensive comparison of an isotope‐incorporated atmospheric general circulation model with vapor isotopologue ratio observation data by two quasi‐global satellite sensors in preparation for data assimilation of water isotope ratios. A global Isotope‐incorporated Global Spectral Model simulation nudged toward the reanalysis wind field, atmospheric total column data from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) on Envisat, and midtropospheric (800 to 500 hPa) data from Tropospheric Emission Spectrometer (TES) on Aura were used. For the mean climatological δD of both the total atmospheric column and the midtroposphere layer, the model reproduced their geographical variabilities quite well. There is, however, some degree of underestimation of the latitudinal gradient (higher δD in the tropics and lower δD in midlatitudes) compared to the SCIAMACHY data, whereas there is generally less disagreement except lower δD over the Maritime Continent compared to the TES data. It was also found that the two satellite products have different relationships between water vapor amount and isotopic composition. Particularly, atmospheric column mean δD, which is dominated by lower‐tropospheric vapor, closely follows the fractionation pattern of a typical Rayleigh‐type “rain out” process, whereas in the midtroposphere the relationship between isotopic composition and vapor amount is affected by a “mixing” process. This feature is not reproduced by the model, where the relationships between δD and the vapor are similar to each other for the atmospheric column and midtroposphere. Comparing on a shorter time scale, it becomes clear that the data situation for future data assimilation for total column δD is most favorable for tropical and subtropical desert areas (i.e., Sahel, southern Africa, mideastern Asia, Gobi, Australia, and the southwest United States), whereas the available midtropospheric δD observations cover wider regions, particularly over tropical to subtropical oceans.

Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when and how carbon dioxide is exchanged between the land and atmosphere. Terrestrial gross primary production (GPP) constitutes the largest flux component in the global carbon budget, however significant uncertainties remain in GPP estimates and its seasonality. Empirically, we show that global spaceborne observations of solar induced chlorophyll fluorescence - occurring during photosynthesis - exhibit a strong linear correlation with GPP. We found that the fluorescence emission even without any additional climatic or model information has the same or better predictive skill in estimating GPP as those derived from traditional remotely-sensed vegetation indices using ancillary data and model assumptions. In boreal summer the generally strong linear correlation between fluorescence and GPP models weakens, attributable to discrepancies in savannas/croplands (18-48% higher fluorescence-based GPP derived by simple linear scaling), and high-latitude needleleaf forests (28-32% lower fluorescence). Our results demonstrate that retrievals of chlorophyll fluorescence provide direct global observational constraints for GPP and open an entirely new viewpoint on the global carbon cycle. We anticipate that global fluorescence data in combination with consolidated plant physiological fluorescence models will be a step-change in carbon cycle research and enable an unprecedented robustness in the understanding of the current and future carbon cycle. Citation: Frankenberg, C., et al. (2011), New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity, Geophys. Res. Lett., 38, L17706, doi: 10.1029/2011GL048738.

The column-average dry air mole fractions of atmospheric carbon dioxide and methane (X_(CO_2) and X_(CH_4)) are inferred from observations of backscattered sunlight conducted by the Greenhouse gases Observing SATellite (GOSAT). Comparing the first year of GOSAT retrievals over land with colocated ground-based observations of the Total Carbon Column Observing Network (TCCON), we find an average difference (bias) of −0.05% and −0.30% for X_(CO_2) and X_(CH_4) with a station-to-station variability (standard deviation of the bias) of 0.37% and 0.26% among the 6 considered TCCON sites. The root-mean square deviation of the bias-corrected satellite retrievals from colocated TCCON observations amounts to 2.8 ppm for X_(CO_2) and 0.015 ppm for X_(CH_4). Without any data averaging, the GOSAT records reproduce general source/sink patterns such as the seasonal cycle of X_(CO_2) suggesting the use of the satellite retrievals for constraining surface fluxes.

Global retrieval of solar induced fluorescence emitted by terrestrial vegetation can provide an unprecedented measure for photosynthetic efficiency. The GOSAT (JAXA, launched Feb. 2009) and OCO-2 (NASA, to be launched 2013) satellites record high-resolution spectra in the O_2 A-band region, overlapping part of the chlorophyll fluorescence spectrum. We show that fluorescence cannot be unambiguously discriminated from atmospheric scattering effects using O_2 absorption lines. This can cause systematic biases in retrieved scattering parameters (aerosol optical thickness, aerosol height, surface pressure, surface albedo) if fluorescence is neglected. Hence, we demonstrate an efficient alternative fluorescence least-squares retrieval method based solely on strong Fraunhofer lines in the vicinity of the O_2 A-band, disentangling fluorescence from scattering effects. Not only does the Fraunhofer line fit produce a more accurate estimate of fluorescence emission, but it also allows improved retrievals of atmospheric aerosols from the O_2 A-band.

Monitoring of atmospheric methane (CH_4) concentrations from space-based instruments such as the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and the Greenhouse Gases Observing Satellite (GOSAT) relies on observations of sunlight backscattered to space by the Earth's surface and atmosphere. Retrieval biases occur due to unaccounted scattering effects by aerosols and thin cirrus that modify the lightpath. Here, we evaluate the accuracy of two retrieval methods that aim at minimizing such scattering induced errors. The lightpath “proxy” method, applicable to SCIAMACHY and GOSAT, retrieves CH4 and carbon dioxide (CO_2) simultaneously and uses CO_2 as a proxy for lightpath modification. The “physics-based” method, which we propose for GOSAT, aims at simultaneously retrieving CH_4 concentrations and scattering properties of the atmosphere. We evaluate performance of the methods against a trial ensemble of simulated aerosol and cirrus loaded scenes. More than 80% of the trials yield residual scattering induced CH_4 errors below 0.6% and 0.8% for the proxy and the physics-based approach, respectively. Very few cases result in errors greater than 2% for both methods. Advantages of the proxy approach are efficient and robust performance yielding more useful retrievals than the physics-based method which reveals some nonconvergent cases. The major disadvantage of the proxy method is the uncertainty of the proxy CO_2 concentration contributing to the overall error budget. Residual errors generally correlate with particle and surface properties and thus might impact inverse modeling of CH_4 sources and sinks.

The hydrological cycle and its response to environmental variability such as temperature changes is of prime importance for climate reconstruction and prediction. We retrieved deuterated water/water (HDO/H2O) abundances using spaceborne absorption spectroscopy, providing an almost global perspective on the near-surface distribution of water vapor isotopologs. We observed an unexpectedly high HDO/H2O seasonality in the inner Sahel region, pointing to a strong isotopic depletion in the subsiding branch of the Hadley circulation and its misrepresentation in general circulation models. An extension of the analysis at high latitudes using ground-based observations of and a model study shows that dynamic processes can entirely compensate for temperature effects on the isotopic composition of precipitation

Methane retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument onboard ENVISAT provide important information on atmospheric CH4 sources, particularly in tropical regions which are poorly monitored by in situ surface observations. Recently, Frankenberg et al. (2008a, 2008b) reported a major revision of SCIAMACHY retrievals due to an update of spectroscopic parameters of water vapor and CH4. Here, we analyze the impact of this revision on global and regional CH4 emissions estimates in 2004, using the TM5-4DVAR inverse modeling system. Inversions based on the revised SCIAMACHY retrievals yield ~20% lower tropical emissions compared to the previous retrievals. The new retrievals improve significantly the consistency between observed and assimilated column average mixing ratios and the agreement with independent validation data. Furthermore, the considerable latitudinal and seasonal bias correction of the previous SCIAMACHY retrievals, derived in the TM5-4DVAR system by simultaneously assimilating high-accuracy surface measurements, is reduced by a factor of ~3. The inversions result in significant changes in the spatial patterns of emissions and their seasonality compared to the bottom-up inventories. Sensitivity tests were done to analyze the robustness of retrieved emissions, revealing some dependence on the applied a priori emission inventories and OH fields. Furthermore, we performed a detailed validation of simulated CH4 mixing ratios using NOAA ship and aircraft profile samples, as well as stratospheric balloon samples, showing overall good agreement. We use the new SCIAMACHY retrievals for a regional analysis of CH4 emissions from South America, Africa, and Asia, exploiting the zooming capability of the TM5 model. This allows a more detailed analysis of spatial emission patterns and better comparison with aircraft profiles and independent regional emission estimates available for South America. Large CH4 emissions are attributed to various wetland regions in tropical South America and Africa, seasonally varying and opposite in phase with CH4 emissions from biomass burning. India, China and South East Asia are characterized by pronounced emissions from rice paddies peaking in the third quarter of the year, in addition to further anthropogenic emissions throughout the year

Methane retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument onboard ENVISAT provide important information on atmospheric CH4 sources, particularly in tropical regions which are poorly monitored by in situ surface observations. Recently, Frankenberg et al. (2008a, 2008b) reported a major revision of SCIAMACHY retrievals due to an update of spectroscopic parameters of water vapor and CH4. Here, we analyze the impact of this revision on global and regional CH4 emissions estimates in 2004, using the TM5-4DVAR inverse modeling system. Inversions based on the revised SCIAMACHY retrievals yield ~20% lower tropical emissions compared to the previous retrievals. The new retrievals improve significantly the consistency between observed and assimilated column average mixing ratios and the agreement with independent validation data. Furthermore, the considerable latitudinal and seasonal bias correction of the previous SCIAMACHY retrievals, derived in the TM5-4DVAR system by simultaneously assimilating high-accuracy surface measurements, is reduced by a factor of ~3. The inversions result in significant changes in the spatial patterns of emissions and their seasonality compared to the bottom-up inventories. Sensitivity tests were done to analyze the robustness of retrieved emissions, revealing some dependence on the applied a priori emission inventories and OH fields. Furthermore, we performed a detailed validation of simulated CH4 mixing ratios using NOAA ship and aircraft profile samples, as well as stratospheric balloon samples, showing overall good agreement. We use the new SCIAMACHY retrievals for a regional analysis of CH4 emissions from South America, Africa, and Asia, exploiting the zooming capability of the TM5 model. This allows a more detailed analysis of spatial emission patterns and better comparison with aircraft profiles and independent regional emission estimates available for South America. Large CH4 emissions are attributed to various wetland regions in tropical South America and Africa, seasonally varying and opposite in phase with CH4 emissions from biomass burning. India, China and South East Asia are characterized by pronounced emissions from rice paddies peaking in the third quarter of the year, in addition to further anthropogenic emissions throughout the year

N_2-broadened half widths and pressure shifts were obtained for transitions in the 2ν_3 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 N_2 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.

The EVERGREEN project, funded by the European Commission 5th Framework Environmental Programme for better exploitation of Earth Observation data, has demonstrated the benefits of new methods for the exploitation of satellite data in global climate and air pollution research and application. In particular, SCIAMACHY on board the European Earth Observation satellite ENVISAT has derived the first greenhouse gas emissions from space, generated ozone measurements to improve the weather forecast and delivered an operational service for air pollution monitoring and predictions. In this paper the results of the EVERGREEN project are summarised concentrating on the SCIAMACHY measurements of methane (CH4), carbon dioxide (CO2) and carbon monoxide (CO) in the troposphere. But also the measurements by MIPAS of the vertical distribution of these gases in the upper troposphere and lower stratosphere are analysed. Both SCIAMACHY and MIPAS are spectrometers on board the ESA environmental satellite ENVISAT, which was launched March 1st, 2002 with a scheduled operational life time of 5 years. The measurements by MOPITT, a Canadian instrument on the NASA EOS Terra satellite launched in December 1999, provide additional information on tropospheric carbon monoxide. Global, regional, yearly and seasonal variations of CH4, CO2 and CO over the years 2003-2005 are analysed and compared with atmospheric models and ground based measurements. First inverse modelling studies using the new satellite datasets suggest some significant discrepancies of CH4 and CO emission compared to bottom-up inventories., JRC.H.2-Climate change

We extend the analysis of a global CH4 data set retrieved from SCIAMACHY (Frankenberg et al., 2006) by making a detailed comparison with inverse TM5 model simulations for 2003 that are optimized versus high accuracy CH4 surface measurements from the NOAA ESRL network. The comparison of column averaged mixing ratios over remote continental and oceanic regions shows that major features of the atmospheric CH4 distribution are consistent between SCIAMACHY observations and model simulations. However, the analysis suggests that SCIAMACHY CH4 retrievals may have some bias that depends on latitude and season (up to 30 ppb). Large enhancements of column averaged CH4 mixing ratios ( 50–100 ppb) are observed and modeled over India, Southeast Asia, and the tropical regions of South America, and Africa. We present a detailed comparison of observed spatial patterns and their seasonal evolution with TM5 1 1 zoom simulations over these regions. Application of a new wetland inventory leads to a significant improvement in the agreement between SCIAMACHY retrievals and model simulations over the Amazon basin during the first half of the year. Furthermore, we present an initial coupled inversion that simultaneously uses the surface and satellite observations and that allows the inverse system to compensate for the potential systematic bias. The results suggest significantly greater tropical emissions compared to either the a priori estimates or the inversion based on the surface measurements only. Emissions from rice paddies in India and Southeast Asia are relatively well constrained by the SCIAMACHY data and are slightly reduced by the inversion.

In the framework of the European EVERGREEN project, three scientific algorithms, namely WFM-DOAS, IMAPDOAS and IMLM, have been developed to retrieve the total column amounts of key atmospheric trace gases including CO and CH_4 from SCIAMACHY nadir observations in its near-infrared channels. These channels offer the capability to detect trace gases in the planetary boundary layer, potentially making the associated retrieval products suited for regional source-sink studies. The retrieval products of these three algorithms, in their present status of development, have been compared to independent data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers, for the year 2003. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of coincidences that satisfy the temporal and spatial collocation criteria, the individual SCIAMACHY data points have been compared with a 3rd order polynomial interpolation of the ground-based data with time. Particular attention has been paid to the question whether the products reproduce correctly the seasonal and latitudinal variabilities of the target species. We present an overall assessment of the data quality of the currently available latest versions of the CO and CH4 total column products from the three scientific retrieval algorithms.

The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO_2 in combination with modeled column-averaged CO_2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget.

International audience; Total column amounts of CO, CH4, CO2 and N2O retrieved from SCIAMACHY nadir observations in its near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.5 for CO and CH4 and version 0.4 for CO2 and N2O), IMAP-DOAS (version 1.1 and 0.9 (for CO)) and IMLM (version 6.3) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The present results indicate that the individual SCIAMACHY data obtained with the actual versions of the algorithms have been significantly improved, but that the quality requirements, for estimating emissions on regional scales, are not yet met. Nevertheless, possible directions for further algorithm upgrades have been identified which should result in more reliable data products in a near future.

Total column amounts of CO, CH_4, CO_2 and N_2O retrieved from SCIAMACHY nadir observations in its near-infrared channels have been compared to data from a ground-based quasi-global network of Fourier-transform infrared (FTIR) spectrometers. The SCIAMACHY data considered here have been produced by three different retrieval algorithms, WFM-DOAS (version 0.5 for CO and CH_4 and version 0.4 for CO_2 and N_2O), IMAP-DOAS (version 1.1 and 0.9 (for CO)) and IMLM (version 6.3) and cover the January to December 2003 time period. Comparisons have been made for individual data, as well as for monthly averages. To maximize the number of reliable coincidences that satisfy the temporal and spatial collocation criteria, the SCIAMACHY data have been compared with a temporal 3rd order polynomial interpolation of the ground-based data. Particular attention has been given to the question whether SCIAMACHY observes correctly the seasonal and latitudinal variability of the target species. The present results indicate that the individual SCIAMACHY data obtained with the actual versions of the algorithms have been significantly improved, but that the quality requirements, for estimating emissions on regional scales, are not yet met. Nevertheless, possible directions for further algorithm upgrades have been identified which should result in more reliable data products in a near future.

The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO2 in combination with modeled column-averaged CO2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry–transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget.

We investigate whether a recently proposed large source of CH4 from vegetation can be reconciled with atmospheric measurements. Atmospheric transport model simulations with and without vegetation emissions are compared with background CH4, d13C-CH4 and satellite measurements. For present–day CH4 we derive an upper limit to the newly discovered source of 125 Tg CH4 yr 1. Analysis of preindustrial CH4, however, points to 85 Tg CH4 yr 1 as a more plausible limit. Model calculations with and without vegetation emissions show strikingly similar results at background surface monitoring sites, indicating that these measurements are rather insensitive to CH4 from plants. Simulations with 125 Tg CH4 yr 1 vegetation emissions can explain up to 50% of the previously reported unexpectedly high CH4 column abundances over tropical forests observed by SCIAMACHY. Our results confirm the potential importance of vegetation emissions, and call for further research.