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

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (similar to biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH(4) yr(-1), range 540-568. About 60% of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH(4) yr(-1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (similar to 64% of the global budget, amp;lt;30 degrees N) as compared to mid (similar to 32 %, 30-60 degrees N) and high northern latitudes (similar to 4 %, 60-90 degrees N). Top-down inversions consistently infer lower emissions in China (similar to 58 TgCH(4) yr(-1), range 51-72, -14 %) and higher emissions in Africa (86 TgCH(4) yr(-1), range 73-108, + 19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project. Funding Agencies|Swiss National Science Foundation; NASA [NNX14AF93G, NNX14AO73G]; National Environmental Science Program - Earth Systems and Climate Change Hub; European Commission [283576, 633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; US Department of Energy, BER [DE-AC02-05CH11231]; FAO member countries; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1502]; ERC [322998]; NERC [NE/J00748X/1]; Swedish Research Council VR; Research Council of Norway [244074]; NSF [1243232, 1243220]; National Science and Engineering Research Council of Canada (NSERC); Chinas QianRen Program; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organization (CSIRO Australia); Bureau of Meteorology (Australia); Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]

We extend the analysis of a global CH_4 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 CH_4 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 CH_4 distribution are consistent between SCIAMACHY observations and model simulations. However, the analysis suggests that SCIAMACHY CH_4 retrievals may have some bias that depends on latitude and season (up to ∼30 ppb). Large enhancements of column averaged CH_4 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.

The geographic and seasonal emission distributions of the major sources and sinks of atmospheric methane were compiled using methane flux measurements and energy and agricultural statistics in conjunction with global digital data bases of land surface characteristics and anthropogenic activities. Chemical destruction of methane in the atmosphere was calculated using three-dimensional OH fields every 5 days taken from Spivakovsky et al. (1990a, b). The signatures of each of the sources and sinks in the atmosphere were simulated using a global three-dimensional tracer transport model. Candidate methane budget scenarios were constructed according to mass balance of methane and its carbon isotopes. The verisimilitude of the scenarios was tested by their ability to reproduce the meridional gradient and seasonal variations of methane observed in the atmosphere. Constraints imposed by all the atmospheric observations are satisfied simultaneously by several budget scenarios. A preferred budget comprises annual destruction rates of 450 Tg by OH oxidation and 10 Tg by soil absorption and annual emissions of 80 Tg from fossil sources, 80 Tg from domestic animals, and 35 Tg from wetlands and tundra poleward of 50°N. Emissions from landfills, tropical swamps, rice fields, biomass burning, and termites total 295 Tg; however, the individual contributions of these terms cannot be determined uniquely because of the lack of measurements of direct fluxes and of atmospheric methane variations in regions where these sources are concentrated.