Search

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.

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.

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.

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.

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.

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.