The Global Methane Budget 2000–2017

Abstract

Abstract. Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). Assessing the relative importance of CH4 in comparison to CO2 is complicated by its shorter atmospheric lifetime, stronger warming potential, and atmospheric growth rate variations over the past decade, the causes of which are still debated. Two major difficulties in reducing uncertainties arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (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 new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (top-down approach) to be 572 Tg CH4 yr−1 (range 538–593, corresponding to the minimum and maximum estimates of the ensemble), of which 357 Tg CH4 yr−1 or ~ 60 % are attributed to anthropogenic sources (range 50–65 %). This total emission is 27 Tg CH4 yr−1 larger than the value estimated for the period 2000–2009 and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for the period 2003–2012 (Saunois et al. 2016). Since 2012, global CH4 emissions have been tracking the carbon intensive scenarios developed by the Intergovernmental Panel on Climate Change (Gidden et al., 2019). Bottom-up methods suggest larger global emissions (737 Tg CH4 yr−1, range 583–880) than top-down inversion methods, mostly because of larger estimated natural emissions from sources such as natural wetlands, other inland water systems, and geological sources. However the strength of the atmospheric constraints on the top-down budget, suggest that these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric-based emissions indicates a predominance of tropical emissions (~ 65 % of the global budget,

Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement

“…A challenge to policy-making is that there is a major discrepancy (Leip et al, 2018;Saunois et al, 2016Saunois et al, , 2019 between "top-down" estimates (Nisbet & Weiss, 2010) of the annual global methane emission, assessed from measuring the atmosphere, and "bottom-up" totals summing emissions estimates based on national data (e.g., number of cows and area of rice fields): the "bottom-up" numbers are typically much higher (e.g. EDGAR-European Commission, Joint Research Centre / Netherlands Environmental Assessment Agency, 2011).…”

Investigation of the global methane budget over 1980–2017 using GFDL-AM4.1

“…While anthropogenic activities are widely considered responsible for the long-term methane increase since preindustrial times (Dlugokencky et al, 2011), there is no consensus on the drivers for the methane stabilization during 1999-2006 and renewed growth since 2007. Previous studies have attributed the stabilization during 1999-2006 to the combined effects of increased anthropogenic emissions with decreased wetland emissions (Bousquet et al, 2006), decreased fossil fuel emissions (Dlugokencky et al, 2003;Simpson et al, 2012;Schaefer et al, 2016) or rice paddies emissions (Kai et al, 2011), stable emissions from microbial and fossil fuel sources (Levin et al, 2012), or variations in methane sinks (Rigby et al, 2008;Montzka et al, 2011;Schaefer et al, 2016). The observed renewed growth since 2007 has been explained alternatively through increases in tropical emissions (Houweling et al, 2014;Nisbet et al, 2016) such as agricultural emissions (Schaefer et al, 2016;Patra et al, 2016) and tropical wetland emissions (Bousquet et al, 2011;Maasakkers et al, 2019), increases in fossil fuel emissions (Rice et al, 2016;Worden et al, 2017), decreases in sources compensated by decreases in sinks due to OH levels (Turner et al, 2017;Rigby et al, 2017), or a combination of changes in different sources such as increases in fossil, agriculture, and waste emissions with decreases in biomass burning emissions (Saunois et al, 2017).…”

“…Previous work has generally combined observations of methane and its isotopic composition (δ 13 CH 4 ) with inverse models (top-down), process-based models (bottom-up), or box models to estimate methane emissions and sinks and their variability (Bousquet et al, 2006;Monteil et al, 2011;Rigby et al, 2012;Kirschke et al, 2013;Ghosh et al, 2015;Schwietzke et al, 2016;Schaefer et al, 2016;Nisbet et al, 2014Nisbet et al, , 2016Dalsøren et al, 2016;Turner et al, 2017;Rigby et al, 2017). Inverse models use observations to derive emissions, but usually prescribe climatological OH, O( 1 D), and Cl levels or loss rates (e.g., Rice et al, 2016;Tsuruta et al, 2017).…”

“…Inverse models use observations to derive emissions, but usually prescribe climatological OH, O( 1 D), and Cl levels or loss rates (e.g., Rice et al, 2016;Tsuruta et al, 2017). Box models, on the other hand, use methane observations together with those of other proxy chemicals (e.g., 13 C/ 12 C ratio, ethane, carbon monoxide, methyl chloroform) to provide information on the global methane budget (e.g., Schaefer et al, 2016;Turner et al, 2017) but lack information on spatial variability or regional characteristics. With process-based models (e.g., wetlands) and inventories representing different source types (e.g., fossil fuel emissions) to drive chemical transport models, the bottom-up approach is able to estimate the methane budget for all individual sources and sinks.…”

Method of assessment generation efficiency at thermal power plants taking into account emissions of pollutants

“…Подобные сводки собираются в ВМО с ряда метеорологических организаций США и Великобритании, а также Европейского центра среднесрочных прогнозов и Японского метеорологического агентства, где они обрабатываются и передаются в ООН. Данные 2018 г. по всем видам парниковых газов, в том числе по CO 2 [4,5], CH 4 [6][7][8][9][10], SF 6 [11] показали существенное превышение показателей 2017 г., который считался рекордным. Очевидно, что это не позволяет остановиться на двухградусном превышении средней температуры атмосферы, и тем более -на полутораградусном, как было представлено на Парижской конференции по инициативе Генсека ООН 1 в связи с недостаточностью, по его мнению, усилий стран в борьбе с изменением климата.…”