The global average methane mole fractions are from four in situ atmospheric observation networks: the Earth System Research Laboratory from the US National Oceanic and Atmospheric Administration (NOAA ESRL; Dlugokencky et al., 1994), the Advanced Global Atmospheric Gases Experiment (AGAGE; Rigby et al., 2008), the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Francey et al., 1999), and the University of California, Irvine (UCI; Simpson et al., 2012). The four networks show a consistent evolution of the globally averaged methane mole fractions (Fig. 1a). The methane mole fractions refer here to the same NOAA2004A CH4 reference scale. The different sampling sites used to compute the global average and the sampling frequency may explain the observed differences between networks. Indeed, the UCI network samples atmospheric methane in the Pacific Ocean between 71∘ N and 47∘ S using flasks during specific campaign periods, while other networks use both continuous and flask measurements worldwide. During the first half of the 2000s, the methane mole fraction remained relatively stable (1770–1785 ppb), with small positive growth rate until 2007 (0.6 ± 0.1 ppb yr-1, Fig. 1b). Since 2007, methane atmospheric mole fraction rose again, reaching 1820 ppb in 2012. A mean growth rate of 5.2 ± 0.2 ppb yr-1 over the period 2008–2012 is observed (Fig. 1b).

As found in several studies (e.g. Bousquet et al., 2006), the flux anomaly (see Supplement, Sect. 2) from top-down inversions (Fig. 1d) is found more robust than the total source estimate when comparing different inversions (Fig. 1c). The mean range between the inverse estimates of total global emissions (Fig. 1c) is of 35 Tg CH4 yr-1 (14 to 54 over the years and inversions reported here); this means that the uncertainty in the total annual global methane emissions inferred by top-down approaches is about 6 % (35 Tg CH4 yr-1 over 550 Tg CH4 yr-1). It is to be noted that this rather good agreement between these estimates is linked with the associated rather small range of global sinks. Indeed, most inversions use similar methyl chloroform (MCF)-constrained OH fields and temperature fields. The three top-down studies spanning 2000 to 2012 (Table 1) show an increase of 15 to 33 Tg CH4 yr-1 between 2000 and 2012 (Fig. 1d). Despite the increase in global methane emissions being of the order of magnitude of the range between the models, flux anomalies clearly show that all individual inversions infer an increase in methane emissions over the period 2000–2012 (Fig. 1d). The inversions using satellite observations included here mainly use GOSAT retrievals (starting from mid-2009), and only one inversion is constrained with SCIAMACHY column methane mole fractions (from 2003 but ending in 2012, dashed lines in Fig. 1d). On average, satellite-based inversions infer higher annual emissions than surface-based inversions (+12 Tg CH4 yr-1 higher over 2010–2012) as previously shown in Saunois et al. (2016) and Locatelli et al. (2015). Also, it is worth noting that the ensemble of top-down results shows emissions that are consistently lower in 2009 and higher in 2008 and 2010 (Figs. 1c and S1 in the Supplement).

When averaging the anomalies in global emissions over the inversions, we find a difference of 22 [5–37] Tg CH4 between the yearly averages for 2000 and 2012 (Fig. 2a). Over the period 2000–2012, the variations in emission anomalies reveal both year-to-year changes and a positive long-term trend. Year-to-year changes are found to be the largest in the tropics: up to ±15 Tg CH4 yr-1 (Fig. 2b), with a negative anomaly in 2004–2006 and a positive anomaly after 2007 visible in all inversions except one (Fig. 1d). Compared with the tropical signal, mid-latitude emissions exhibit smaller anomalies (mean anomaly mostly below 5 Tg CH4 yr-1, except around 2005) but contribute a rather sharp increase in 2006–2008, marking a transition between the period 2002–2006 and the period 2008–2012 at the global scale (Fig. 2a and c). The boreal regions do not contribute significantly to year-to-year changes, except in 2007, as already noted in several studies (Dlugokencky et al., 2009; Bousquet et al., 2011).

When splitting global methane emissions into anthropogenic and natural emissions at the global scale (Fig. 2e and f, respectively), both of these two general categories show significant year-to-year changes. As natural and anthropogenic emissions occur concurrently in several regions, top-down approaches have difficulty in separating their contribution. Therefore the year-to-year variability allocated to anthropogenic emissions from inversions may be an artefact of our separation method (see Sect. 2) and/or reflect the larger variability between studies compared to natural emissions. However, some of the anthropogenic methane sources are sensitive to climate, such as rice cultivation or biomass burning, and also, to a lesser extent, enteric fermentation and waste management. Fossil fuel exploitation can also be sensitive to rapid economic changes, and meteorological variability may impact the fuel demand for heating and cooling systems. However, anthropogenic emissions reported by bottom-studies (black line on Fig. 2e) show much fewer year-to-year changes than inferred by top-down inversions (blue line of Fig. 2e). China coal production rose faster from 2002 until 2011, when its production started to stabilise or even decline (IEA, 2016). This last period is characterised by major reorganisations in the Chinese coal industry, including evolution from many small gassy mines to fewer mines with better safety and emission control. The global natural gas production steadily increased over time despite a short drop in production in 2009 following the economic crisis (IEA, 2016). The bottom-up inventories do reflect some of this variation, such as in 2009 when gas and oil methane emissions slightly decreased (EDGARv4.2FT2010 and EDGARv4.2EXT, Fig. S7). Methane emissions from agriculture and waste are continuously growing in the bottom-up inventories at the global scale. The observed activity data underlying the emissions from agriculture estimated in this study, as reported by countries to FAO via the FAOSTAT database (FAO, 2017a, b), exhibit inter-annual variabilities that partly explain the variability in methane emissions discussed herein. Livestock methane emissions from the Americas (mainly South America) increased mainly between 2000 and 2004 and remained stable afterwards (estimated by FAOSTAT, Fig. S12). Additionally, Asian (India, China, and South and East Asia) livestock emissions mainly increased between 2004 and 2008 and also remained rather stable afterwards. In contrast, livestock emissions in Africa increased continuously over the full period. These continental variations translate into global livestock emissions increasing continuously over the full period, though at a slower rate after 2008 (Fig. S13). Overall, these anthropogenic emissions exhibit more semi-decadal to decadal evolutions (see below) than year-to-year changes as found in top-down inversions.

For natural sources, the mean anomaly of the top-down ensemble suggests year-to-year changes ranging ±10 Tg CH4 yr-1, which is lower than but in phase with the total source mean anomaly. The mean anomaly of global natural sources inferred by top-down studies is negative around 2005 and positive around 2007 (Fig. 2f). The year-to-year variation in wetland emissions inferred from land surface models is of the same order of magnitude but out of phase compared to the ensemble mean top-down estimates (Fig. 2f). However, some individual top-down approaches suggest anomalies smaller than or of different sign than the mean of the ensemble (Fig. S2). Also, some land surface models show anomalies in better agreement with the top-down ensemble mean in 2000–2006 (Fig. S11). The 2009 (2010) negative (positive) anomaly in wetland emissions is common to all land surface models (Fig. S11) and is the result of variations in flooded areas (mainly in the tropics) and in temperature (mainly in boreal regions) (Poulter et al., 2017). Overall, from the contradictory results from top-down and bottom-up approaches, it is difficult to draw any robust conclusions on the year-to-year variations in natural methane emissions over the period 2000–2012.

The mean anomaly of the inversion estimates shows a positive linear trend in global emissions of +2.2 ± 0.2 Tg CH4 yr-2 over 2000–2012 Fig. 2a). It originates mainly from increasing tropical emissions (+1.6 ± 0.1 Tg CH4 yr-2, Fig. 2b) with a smaller contribution from the mid-latitudes (+0.6 ± 0.1 Tg CH4 yr-2, Fig. 2c). The positive global trend is explained mostly by an increase in anthropogenic emissions, as separated in inversions (+2.0 ± 0.1 Tg CH4 yr-2, Fig. 2e). This represents an increase of about 26 Tg CH4 in the annual anthropogenic emissions between 2000 and 2012, casting serious doubt on the bottom-up methane inventories for anthropogenic emissions, showing an increase in anthropogenic emissions of +55 [45–73] Tg CH4 between 2000 and 2012, with USEPA and GAINS inventories at the lower end and EDGARv4.2FT2012 at the higher end of the range. This possible overestimation of the recent anthropogenic emissions increase by inventories has already been suggested in individual studies (e.g. Patra et al., 2011; Bergamaschi et al., 2013; Bruhwiler et al., 2014; Thompson et al., 2015; Peng et al., 2016; Saunois et al., 2016) and is confirmed in this study as a robust feature. Splitting the anthropogenic sources into the components identified in the method section, the trend in anthropogenic emissions from top-down studies mainly originates from the agriculture and waste sector (+1.2 ± 0.1 Tg CH4 yr-2, Fig. 3a). Adding the fossil fuel emission trend almost matches the global trend of anthropogenic emissions (Fig. 3b). It should be noted here that the individual inversions all suggest constant to increasing emissions from agriculture and waste handling (Fig. S3), while some suggest constant to decreasing emissions from fossil fuel use and production (Fig. S4). The latter result seems surprising in view of large increases in coal production during 2000–2012, especially in China. However, this recent period is characterised by major reorganisations in the Chinese coal industry, including evolution from many small gassy mines to fewer mines with better safety and emission control. The trend in biomass burning emissions is small but barely significant between 2000 and 2012 (-0.05 ± 0.05 Tg CH4 yr-2, Fig. 3). This result is consistent with the GFED dataset (both versions 3 and 4s) for which no significant trend was found over this 13-year period. However, between 2002 and 2010, a significant negative trend of -0.5 ± 0.1 Tg CH4 yr-2 is found for biomass burning, both from the top-down approaches (Fig. S5) and the GFED3 and GFED4s inventory (Fig. S10); this corresponds to dry years in the tropics. Although it should be noted that almost all inversions use GFED3 in their prior fluxes (Table S1) and therefore are not independent from the bottom-up estimates Over the 13-year period, the wetland emissions in the inversions show a small positive trend (+0.2 ± 0.1 Tg CH4 yr-2) about twice as large as the trends of emissions from land surface models but within the range of uncertainty (+0.1 ± 0.1 Tg CH4 yr-2, Poulter et al., 2017). As stated previously, the wetland emissions from some land surface models disagree with the ensemble mean of land surface models (Fig. S11).

According to Fig. 2a, the period 2000–2012 is split into two parts – before 2006 and after 2008. Neither a significant nor a systematic trend in the global total sources (among the inversions of Fig. 1d) is observed before 2006, likewise after 2008 (see Fig. S6 for individual calculated trends); although large year-to-year variations are visible. Before 2006, anthropogenic emissions show a positive trend of +2.4 ± 0.2 Tg CH4 yr-2, compensated for by decreasing natural emissions (-2.4 ± 0.2 Tg CH4 yr-2; calculated from Fig. 2e and f), which explains the rather stable global total emissions. Bousquet et al. (2006) discussed such compensation between 1999 and 2003. The behaviour of the top-down ensemble mean is consistent with a decrease in microbial emissions in 2000–2006, especially in the Northern Hemisphere as suggested by Kai et al. (2011) using 13CH4 observations. However, Levin et al. (2012) showed that the isotopic data selection might bias this result, as they found no such decrease when using background site measurements. Indeed, some individual top-down studies still suggest constant emissions from both natural and anthropogenic sources (Figs. S2, S3 and S4) over that period as found by Levin et al. (2012) or Schwietzke et al. (2016), with both also using 13CH4 observations. The different trends in anthropogenic and natural methane emissions among the inversions highlight the difficulties of the top-down approach in separating natural from anthropogenic emissions and also its dependence on prior emissions. All inversions are based on EDGAR inventory (most of them using EDGARv4.2 version, Table S1). However, the estimated posterior anthropogenic emissions can significantly deviate from this common prior estimate. Similarly, inversions based on the same prior wetland fluxes do not systematically infer the same variations in methane total and natural emissions. These different increments from the prior fluxes are constrained by atmospheric observations and qualitatively indicate that inversions can depart from prior estimates. Contrary to the ensemble mean of inversions, the land surface models gathered in this study show on average a small positive trend (+0.7 ± 0.1 Tg CH4 yr-2) during 2000–2006 (calculated from Fig. 2f), with some exceptions in individuals models (Fig. S11). Recently, Schaefer et al. (2016), based on isotopic data, suggested that diminishing thermogenic emissions caused the early 2000s plateau without ruling out variations in the OH sink. However, another scenario explaining the plateau could combine both constant total sources and sinks. Over 2000–2006, no decrease in thermogenic emissions is found in any of the inversions included in our study (Fig. S4). Even using time-constant prior emissions for fossil fuels in the inversions results in robustly inferring increasing fossil fuel emissions after 2000, although lower than when using inter-annually varying prior estimates from inventories (e.g. Bergamaschi et al., 2013).

All inversions show increasing emissions in the second half of the period, after 2006. For the period 2006–2012, most inversions show a significant positive trend (below 5 Tg CH4 yr-2), within 2σ uncertainty for most of the available inversions (see Fig. S6). Most of this positive trend is explained by the years 2006 and 2007, due to both natural and anthropogenic emissions, but appears to be highly sensitive to the period of estimation (Fig. S6). Between 2008 and 2012, neither the total anthropogenic nor the total natural sources present a significant trend, leading to rather stable global total methane emissions (Fig. 2e and f). Overall, these results suggest that emissions shifted between 2006 and 2008 rather than continuously increasing after 2006. The requirement of a step change in the emissions will be further discussed in Sect. 4. Because of this, in the following section, we analyse in more details the emission changes between two time periods: 2002–2006 and 2008–2012 at global and regional scales.

Integrating all inversions covering at least 3 years over each 5-year period, the global methane emissions are estimated at 545 [530–563] Tg CH4 yr-1 on average over 2002–2006 and at 569 [546–581] Tg CH4 yr-1 over 2008–2012. It is worth noting some inversions do not contribute to both periods, leading to different ensembles being used to compute these estimates. Despite the different ensembles (seven studies for 2002–2006 and 10 studies for 2008–2012), the estimate ranges for both periods are similar. Keeping only the five surface-based inversions covering both periods leads to 542 [530–554] Tg CH4 yr-1 on average over 2002–2006 and 563 [546–573] Tg CH4 yr-1 over 2008–2012, showing remarkably consistent values with the ensemble of the top-down studies and also not showing significant impact in the emission differences between the two time periods (see Table S3).

The emission changes between the period 2002–2006 and the period 2008–2012 have been calculated for inversions covering at least 3 years over both 5-year periods (5 inversions) at global, hemispheric, and regional scales (Fig. 4). The regions are the same as in Saunois et al. (2016). The region denoted as “ 90∘ S–30∘ N” is referred to as the tropics despite the southern mid-latitudes (mainly from Oceania and temperate South America) included in this region. However, since the extra-tropical Southern Hemisphere contributes less than 8% to the emissions from the “90∘ S–30∘ N” region, the region primarily represents the tropics.

The global emission increase of +22 [16–32] Tg CH4 yr-1 is mostly tropical (+18 [13–24] Tg CH4 yr-1, or ∼ 80 % of the global increase). The northern mid-latitudes only contribute an increase of +4 [0–9] Tg CH4 yr-1, while the high-latitudes (above 60∘ N) contribution is not significant. However, most inversions rely on surface observations, which poorly represent the tropical continents, as previously noticed by a previous study (e.g. Bousquet et al., 2011). As a result, this tropical signal may partly be an artefact of inversions attributing emission changes to unconstrained regions. Also, the absence of a significant contribution from the Arctic region means that Arctic changes are below the detection limit of inversions. Indeed, the northern high latitudes emitted about 20 [14–24] Tg CH4 yr-1 of methane over 2002–2006 and 22 [15–31] Tg CH4 yr-1 over 2008–2012 (Table 3), but keeping inversions covering at least 3 years over each 5-year period leads to a null emission change in boreal regions.

The geographical partition of the increase in emissions between 2000–2006 and 2008–2012 inferred here is in agreement with Bergamaschi et al. (2013), who found that 50–85 % of the 16–20 Tg CH4 emission increase between 2007–2010 and 2003–2005 came from the tropics and the rest from the Northern Hemisphere mid-latitudes. Houweling et al. (2014) inferred an increase of 27–35 Tg CH4 yr-1 between the 2-year periods before and after July 2006. The ensemble of inversions gathered in this study infer a consistent increase of 30 [20–41] Tg CH4 yr-1 between the same two periods. The derived increase is highly sensitive to the choice of the starting and ending dates of the time period. The study of Patra et al. (2016) based on six inversions found an increase of 19–36 Tg CH4 yr-1 in global methane emissions between 2002–2006 and 2008–2012, which is consistent with our results.

At the regional scale, top-down approaches infer different emission changes both in amplitude and in sign. These discrepancies are due to transport errors in the models and to differences in inverse setups and can lead to several tens of per cent differences in the regional estimates of methane emissions (e.g. Locatelli et al., 2013). Indeed, the recent study of Cressot et al. (2016) showed that, while global and hemispheric emission changes could be detected with confidence by the top-down approaches using satellite observations, their regional attribution is less certain. Thus, it is particularly critical for regional emissions to rely on several inversions, as done in this study, before drawing any robust conclusion. In most of the top-down results (Fig. 4), the tropical contribution to the global emission increase originates mainly in tropical South America (+9 [6–13] Tg CH4 yr-1) and South and East Asia (+5 [-6–10] Tg CH4 yr-1). Central North America (+2 [0–5] Tg CH4 yr-1) and northern Africa (+2 [0–5] Tg CH4 yr-1) contribute less to the tropical emission increase. The sign of the contribution from South and East Asia is positive in most studies (e.g. Houweling et al., 2014), although some studies infer decreasing emission in this region. The disagreement between inversions could result from the lack of measurement stations to constrain the fluxes in Asia (some have appeared inland India and China but only in the last years, Lin et al., 2017), and also from the rapid up-lift of the compounds emitted at the surface to the free troposphere by convection in this region, leading to surface observations missing information on local fluxes (e.g. Lin et al., 2015).

In the northern mid-latitudes a positive contribution is inferred for China (+4 [1–11] Tg CH4 yr-1) and Central Eurasia and Japan (+1  [-1–6] Tg CH4 yr-1). Also, temperate North America does not contribute significantly to the emission changes. Contrary to a large increase in the US emissions suggested by Turner et al. (2016), none of the inversions detect, at least prior to 2013, an increase in methane emissions possible due to increasing shale gas exploitation in the US. Bruhwiler et al. (2017) highlight the difficulty of deriving trends on relatively short term due to, in particular, inter-annual variability in transport.

The inversions agree that emissions changes remained limited in the Arctic region but do not agree on the sign of the emission change over the high northern latitudes, especially over boreal North America; however, they show a consistent small emission decrease in Russia. This lack of agreement between inversions over the boreal regions highlights the weak sensitivity of inversions in these regions where no or little methane emission changes are found to have occurred over the last decade. Changes in wetland emissions associated with sea ice retreat in the Arctic are probably only a few Tg between the 1980s and the 2000s (Parmentier et al., 2015). Also, decreasing methane emissions in sub-Arctic areas that were drying and cooling over 2003–2011 have offset increasing methane emissions in a wetting Arctic and warming summer (Watts et al., 2014). Permafrost thawing may have caused additional methane production underground (Christensen et al., 2004), but changes in the methane flux to the atmosphere have not been detected by continuous atmospheric stations around the Arctic, despite a small increase in late autumn–early winter in methane emission from Arctic tundra (Sweeney et al., 2016). However, unintentional double counting of emissions from different water systems (wetlands, rivers, lakes) may lead to Artic emission growth in the bottom-up studies when little or none exists (Thornton et al., 2016). The detectability of possibly increasing methane emissions from the Arctic seems possible today based on the continuous monitoring of the Arctic atmosphere at a few but key stations (e.g. Berchet et al., 2016; Thonat et al., 2017), but this surface network remains fragile in the long term and would be more robust with additional constraints such as those that will be provided in 2021 by the active satellite mission MERLIN (Pierangello et al., 2016; Kiemle et al., 2014).

The top-down approaches use bottom-up estimates as a priori values. For anthropogenic emissions, most of them use the EDGARv4.2FT2010 inventory and GFED3 emission estimates for biomass burning. Their source of a priori information differs more for the contribution from natural wetlands, geological emissions, and termite sources (Table S1). Here we gathered an ensemble of bottom-up estimates for the changes in methane emissions between 2000–2006 and 2008–2012, combining anthropogenic inventories (EDGARv4.2FT2010, USEPA, and GAINS), five biomass burning emission estimates (GFED3, GFED4s, FINN, GFAS, and FAOSTAT), and wetland emissions from 11 land surface models (see Sect. 2 for the details and Saunois et al., 2016 and Poulter et al., 2017). As previously stated, other natural methane emissions (termites, geological, inland waters) are assumed in these model studies to not contribute significantly to the change between 2000–2006 and 2008–2012, because no quantitative indications are available on such changes and because at least some of these sources are less climate sensitive than wetlands.

The bottom-up estimate of the global emission change between the periods 2000–2006 and 2008–2012 (+21 [5–41] Tg CH4 yr-1, Fig. 4) is comparable but possesses a larger spread than top-down estimates (+22 [16–32] Tg CH4 yr-1). Also, the hemispheric breakdown of the change reveals discrepancies between top-down and bottom-up estimates. The bottom-up approaches suggest a much higher increase in emissions in the mid-latitudes (+17 [6–30] Tg CH4 yr-1) than inversions and a smaller increase in the tropics (+6 [-4–13] Tg CH4 yr-1). The main regions where bottom-up and top-down estimates of emission changes differ are tropical South America, South and East Asia, China, USA, and central Eurasia and Japan.

While top-down studies indicate a dominant increase between 2000–2006 and 2008–2012 in tropical South America (+9 [6–13] Tg CH4 yr-1), the bottom-up estimates (based on an ensemble of 11 land surface models and anthropogenic inventories), in contrast, indicate a small decrease (-2  [-6–2] Tg CH4 yr-1) over the same period (Fig. 4). The decrease in tropical South American emissions found in the bottom-up studies results from decreasing emissions from wetlands (about -2.5 Tg CH4 yr-1, mostly due to a reduction in tropical wetland extent, as constrained by the common inventory used by all models, see Poulter et al., 2017) and biomass burning (about -0.7 Tg CH4 yr-1), partly compensated for by a small increase in anthropogenic emissions (about 1 Tg CH4 yr-1, mainly from agriculture and waste). Most of the top-down studies infer a decrease in biomass burning emissions over this region, exceeding the decrease in a priori emissions from GFED3. Thus, the main discrepancy between top-down and bottom-up is due to microbial emissions from natural wetlands (about 4 Tg CH4 yr-1 on average), agriculture, and waste (about 2 Tg CH4 yr-1 on average) over tropical South America.

The emission increase in South and East Asia for the bottom-up estimates (2 Tg CH4 yr-1) results from a 4 Tg CH4 yr-1 increase (from agriculture and waste for half of it, fossil fuel for one-third, and wetland for the remainder) offset by a decrease in biomass burning emissions (-2 [-4–0] Tg CH4 yr-1). The inversions suggest a higher increase in South and East Asia compared to this 2 Tg CH4 yr-1, mainly due to higher increases in wetland and agriculture and waste sources, with the biomass burning decrease and the fossil fuel increase being similar in the inversions compared to the inventories.

In tropical South America and South and East Asia, wetlands and agriculture and waste emissions may both occur in the same or neighbouring model pixels, making the partitioning difficult for the top-down approaches. Also, these two regions lack surface measurement sites, so the inverse systems are less constrained by the observations. However, the SCIAMACHY-based inversion from Houweling et al. (2014) also infers increasing methane emissions over tropical South America between 2002–2006 and 2008–2012. Further studies based on satellite data or additional regional surface observations (e.g. Basso et al., 2016; Xin et al., 2015) would be needed to better assess methane emissions (and their changes) in these under-sampled regions.

For China, bottom-up approaches suggest a +10 [2–20] Tg CH4 yr-1 emission increase between 2002–2006 and 2008–2012, i.e. a trend of about 1.7 Tg CH4 yr-2 (considering a 10 Tg yr-1 increase over 2004–2010), which is much larger than the top-down estimates. The magnitude of the Chinese emission increase varies among emission inventories and essentially appears to be driven by an increase in anthropogenic emissions (fossil fuel and agriculture and waste emissions). Anthropogenic emission inventories indicate that Chinese emissions increased at a rate of 0.6 Tg CH4 yr-2 in USEPA, 3.1 Tg yr-2 in EDGARv4.2, and 1.5 Tg CH4 yr-2 in GAINS between 2000 and 2012. The increase rate in EDGARv4.2 is too strong compared to a recent bottom-up study that suggests a 1.3 Tg CH4 yr-2 increase in Chinese methane emissions over 2000–2010 (Peng et al., 2016). The revised EDGAR inventory v4.3.2 (not officially released when we write these lines) with region-specific emission factors for coal mining in China gives a mean trend in coal emissions of 1.0 Tg CH4 yr-2 over 2000–2010, which is half the value from the previous version EDGARv4.2FT2010 (Fig. S14). These new estimates are more in line with USEPA inventory and with the top-down approaches (range of 0.3 to 2.0 Tg CH4 yr-2 for the total sources in China over 2000–2012), in agreement with Bergamaschi et al. (2013) who inferred an increase rate of 1.1 Tg CH4 yr-2 over 2000–2010.

Finally, while bottom-up approaches show a small increase in US emissions (+2 [-1–4] Tg CH4 yr-1), top-down studies do not show any significant emission change, and this result holds similarly for central Eurasia and Japan.

In Sect. 3.1, we suggest that a concurrent increase in both natural and anthropogenic emissions over 2006–2008 contribute to the total emission increase between 2002–2006 and 2008–2012. The attribution of this change to different source types remains uncertain in inversions, as methane observations alone do not provide sufficient information to fully separate individual sources (see Introduction). However, as in Saunois et al. (2016), we present here a sectorial view of methane emissions for five general source categories, limited at the global scale (Fig. 5, Table 4), as the regional attribution of emission increase is considered too uncertain (Saunois et al., 2016; Tian et al., 2016).

The top-down studies show a dominant positive contribution from microbial sources, such as agriculture and waste (+10 [7–12] Tg CH4 yr-1), and natural wetlands (+6 [-4–16] Tg CH4 yr-1) as compared to fossil-fuel-related emissions (+7 [-2–16] Tg CH4 yr-1). Biomass burning emissions decreased (-3 [-7–0] Tg CH4 yr-1). Other natural sources show a lower but significant increase (+2  [-2–7] Tg CH4 yr-1). These values are estimated based on the five longest inversions. Taking into account shorter inversions leads to different minimum and maximum values, but the mean values are quite robust (Table S4).

Wetland emission changes estimated by 11 land surface models from Poulter et al. (2017) are near zero, but the stability of this source is statistically consistent with the top-down value considering the large uncertainties of both top-down inversions and bottom-up models (Sects. 3.1 and 4). It is worth noting that, for wetland prior estimates, top-down studies generally rely on climatology from bottom-up approaches (e.g. Matthews and Fung, 1987; Kaplan, 2002) and therefore the inferred trend are more independent from bottom-up models than anthropogenic estimates, which generally rely on inter-annually prescribed prior emissions.

The bottom-up estimated decrease in biomass burning emissions of (-2  [-5–0] Tg CH4 yr-1) is consistent with top-down estimates, albeit smaller. The change in agriculture and waste emissions between 2002–2006 and 2008–2012 in the bottom up inventories is in agreement with the top-down values (+10 [7–13] Tg CH4 yr-1), with about two-third of this being increase from agriculture activities (mainly enteric fermentation and manure management, while rice emissions were fairly constant between these two time periods) and one-third from waste (Table S5). The spread between inventories in the increase in methane emissions from the waste sector is much lower than from agriculture activities (enteric fermentation, manure management, and rice cultivation) (see Table S5). Considering livestock (enteric fermentation and manure) emissions estimated by FAOSTAT, about half of the global increase between 2002–2006 and 2008–2012 originates from Asia (India, China, and South and East Asia) and one-third from Africa.

The changes in fossil-fuel-related emissions in bottom-up inventories between 2002–2006 and 2008–2012 (+17 [11–25] Tg CH4 yr-1) are more than twice the estimate from the top-down approaches (+7 [-2–16] Tg CH4 yr-1). Among the inventories, EDGARv4.2 stands in the higher range, with fossil-fuel-related emissions increasing twice as fast as in USEPA and GAINS. The main contributors to this discrepancy are the emissions from coal mining, which increase 3 times as fast as in EDGARv4.2 than in the two other inventories at the global scale. About half of the global increase in fossil fuel emissions originates from China in the EDGARv4.2 inventory. Thus, most of the difference between top-down and bottom-up originates from coal exploitation estimates in China, which is likely overestimated in EDGARv4.2 as aforementioned (Bergamaschi et al., 2013; Peng et al., 2016; Dalsoren et al., 2016; Patra et al., 2016; Saunois et al., 2016). The release of EDGARv4.3.2 will, at least partly, close the gap between top-down and bottom-up studies. Indeed, in EDGARv4.3.2 coal emissions in China increase by 4.3 Tg CH4 yr-1 between 2002–2006 and 2008–2010 instead of 9.7 Tg CH4 yr-1 in EDGARv4.2FT2010, due to the revision of coal emission factors in China. As a result, the next release of EDGARv4.3.2 should narrow the range and decrease the mean contribution of fossil fuels to emission changes estimated by the bottom-up studies.