Accelerating methane growth rate from 2010 to 2017: leading contributions from the tropics and East Asia

After stagnating in the early 2000s, the atmospheric methane growth rate has been positive since 2007 with a significant acceleration starting in 2014. While causes for previous growth rate variations are still not well determined, this recent increase can be studied with dense surface and satellite observations. Here, we use an ensemble of six multi-tracer atmospheric inversions that have the capacity to assimilate the major tracers in the methane oxidation chain – namely methane, formaldehyde, and carbon monoxide – to simultaneously optimize both the methane sources and sinks at each model grid. 5 We show that the recent surge of the atmospheric growth rate between 2010-2013 and 2014-2017 is most likely explained by an increase of global CH4 emissions by 17.5±1.5 Tg yr−1 (mean±1σ), while variations in CH4 sinks remained small. The inferred emission increase is consistently supported by both surface and satellite observations, with leading contributions from the tropics wetlands (∼35%) and anthropogenic emissions in China (∼20%). Such a high consecutive atmospheric growth rate has not been observed since the 1980s and corresponds to unprecedented global total CH4 emissions. 10


Introduction
Methane (CH 4 ) is an important greenhouse gas highly relevant to climate mitigation, given its stronger warming potential and shorter lifetime than carbon dioxide (CO 2 ) (IPCC, 2013). Atmospheric levels of methane, usually measured as dry air mole fraction, have nearly tripled since the Industrial Revolution according to ice core records (Etheridge et al., 1998;Rubino et al., 2019). This increase is mostly due to increases in anthropogenic emissions from agriculture (ruminant livestock and rice 15 farming), fossil fuel use, and waste processing (Kirschke et al., 2013;Saunois et al., 2016;Schaefer, 2019). A significant portion of methane is also emitted from natural sources, including wetlands, inland freshwaters, geological sources, and biomass (www.esrl.noaa.gov/gmd/ccgg/mbl/). The red curve represents the total column mixing ratio, XCH 4 , seen by GOSAT satellite and averaged from all soundings over the land. The smooth curve fit shows a quadratic fit of the trend that accelerates in the latter part of the study period.
(b) Smooth methane growth rate derived from the time series as shown in (a) following methods of Thoning et al. (1989). the satellite column data show a smoother temporal variation in the global average growth rate. GOSAT X CH4 growth rates over different regions show diverse temporal patterns with a higher variability than the global average (Fig. S1), suggesting that satellite data sampling directly over the source regions could provide valuable information to track regional changes in CH 4 fluxes. Furthermore, species in the oxidation chain of methane, namely methane-formaldehyde-carbon monoxide (CH 4 -HCHO-CO) with their reactions to OH as the common sink path, could provide additional constraints on the OH sink of 55 methane. Recent study has shown that HCHO levels can inform about remote tropospheric OH concentrations (Wolfe et al., 2019), and the feedback of CO variations on OH is directly linked to the sink of CH 4 (Gaubert et al., 2017;Nguyen et al., 2020). Hence, satellite retrievals of X HCHO from the Ozone Monitoring Instrument (OMI, (González Abad et al., 2016)) and X CO from the Measurements of Pollution in the Troposphere (MOPITT, (Deeter et al., 2017)) covering the study period could, in theory, provide additional constraints on regional variations of methane sinks. 60 Hence, we developed a multi-tracer variational inverse system, PYVAR-LMDZ, with the capacity to assimilate observations of the CH 4 -HCHO-CO oxidation chain to better constrain the sources and sinks of these species at individual model grid cell (Chevallier et al., 2005;Pison et al., 2009;Fortems-Cheiney et al., 2012;Yin et al., 2015;Zheng et al., 2019). Given observed changes in temporal and spatial variations of all the three species, we optimize simultaneously (i) methane emissions, (ii) CO emissions, (iii) HCHO sources (surface emissions + chemical productions from VOC oxidation), and (iv) OH 65 concentrations. These terms are optimized at a weekly temporal resolution and a 1.9 • by 3.75 • spatial resolution. Besides, we optimize the initial concentrations of all the four species at individual horizontal model grid. Here, we performed an ensemble of six inversions using different combinations of observational constraints (surface vs. satellite, single vs. multiple species) and alternative prior estimates of 3-D OH distributions. With the ensemble results, we aim to (1) identify key regions that contribute to the methane growth rate acceleration from 2010 to 2017, and (2) evaluate the consistency of results inferred from surface 70 and satellite observations. Inversion methods and observational datasets are documented in Section 2. We report estimates of global methane budget change from 2010 to 2017 in Section 3 and discuss regional attributions and sources of uncertainties in Section 4. Section 5 summarizes this work and provides some perspectives for future studies.

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We assimilate surface and satellite methane observations in parallel to test the consistency of information brought by these two types of measurements. We also include versions assimilating HCHO and CO along with CH 4 to test the impacts of adding chemically related species. In total, there are three groups of observational constraints: -S1: Methane and CO measurements from surface stations; -S2: GOSAT X CH4 ; 80 -S3: GOSAT X CH4 , OMI X HCHO , and MOPITT X CO . The assimilation is done from April 2009 to February 2018, and we analyze the results of the eight full years of 2010-2017 with the starting and ending period being spin-up and spin-down phases to avoid edge effect.

Surface Observations
We include surface methane observations from a total of 103 stations (Fig. S2; Table S3) (Prinn et al., 2018)). Measurements from different networks are calibrated to the WMO scale. Daily afternoon averages between 12 and 6 pm local time are used for the assimilation of the continuous in-situ measurements to minimize uncertainties associated with boundary layer height modeling. CO observations from those stations are 90 also assimilated in S1.

Satellite Observations
The TANSO-FTS instrument onboard the Greenhouse Gases Observing Satellite (GOSAT) was launched by The Japan Aerospace Exploration Agency (JAXA) into a polar sun-synchronous orbit in early 2009. It observes column-averaged dry-air carbon dioxide and methane mixing ratios by solar backscatter in the shortwave infrared (SWIR) with near-unit sensitivity across the 95 air column down to surface (Butz et al., 2011;Kuze et al., 2016). Observations are made at a local time around 13:00 with a circular pixel of around 10km in diameter. The distances between pixels both along and cross track are ∼250 km in the default observation mode, and the revisit time for the same observation location is 3 days. Denser observations over particular areas of interest are made in target mode. Here, we use GOSAT X CH4 proxy retrievals (OCPR) version 7.2 from the University of Leicester, which has been well documented and evaluated against various observations. The retrieval has a single-observation 100 precision of 14 ppb (∼0.7%) and a regional bias of ∼4 ppb compared to TCCON stations (Parker et al., 2015(Parker et al., , 2020. This product is also consistent with other GOSAT methane retrievals (Buchwitz et al., 2017). However, we note that there is limited spatial coverage of TCCON stations to fully evaluate GOSAT observations in the high-latitudes and the tropics. We only assimilate GOSAT retrievals over land to minimize potential retrieval biases between nadir and glint viewing modes. The same GOSAT data are assimilated in both S2 and S3.

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For the multi-tracer inversion, S3, we also include OMI X HCHO retrievals version 3 from Smithsonian Astrophysical Observatory (SAO) (González Abad et al., 2016) and MOPITT X CO retrievals version 7 from NCAR (Deeter et al., 2017). All satellite retrievals are processed following the recommend quality flags and the application of corresponding prior profiles and retrieval averaging kernels when provided. We exclude data poleward of 60 • . Individual retrievals that are located in the same model grid within 3-hour intervals are averaged for further assimilation. The observation uncertainty contains the retrieval 110 errors as reported by the data product plus model errors whose standard deviations are empirically set as 1% for CH 4 , 30% for CO, and 30% for HCHO based on previous experiments (Fortems-Cheiney et al., 2012;Cressot et al., 2014;Yin et al., 2015).
Satellite retrievals of the three species (CH4, HCHO, and CO) we use here are generally sensitive to the entire vertical column with some differences toward the lower troposphere. GOSAT XCH4 retrievals using shortwave infrared (SWIR) radiances have approximately uniform sensitivity to methane at all pressure levels (Parker et al., 2015). OMI HCHO retrievals using ul-115 traviolet (UV) radiance are sensitive to the entire column with some decline in the lowest atmospheric layers (González Abad et al., 2016). For MOPITT, we use the multispectral total column CO retrieval products that combine near-infrared (NIR) and thermal infrared (TIR) radiances and hence have an enhanced sensitivity to the lower troposphere (Deeter et al., 2014). Such subtle differences in the vertical sensitivities of the three retrievals as well as their different vertical profiles and lifetimes may influence the ways the observations of the three species inform about OH, which is another source of uncertainty in addition to 120 the model and observation errors.

Ground-based total column measurements
Ground-based X CH4 retrievals from the Total Carbon Column Observing Network (TCCON) from 27 stations are used for an independent evaluation of the posterior model states. TCCON is a network of Fourier transform spectrometers (FTSs) from near-infrared (NIR) solar absorption spectra, designed to retrieve precise total column abundances of CO 2 , CH 4 , N 2 O and CO 125 to validate satellite observations .

Variational Inverse System
We use a Bayesian variational inversion system, PYVAR-LMDz, which uses LMDz-INCA as the chemistry transport model (CTM) (Hourdin et al., 2013;Hauglustaine, 2004). This inversion system has been documented and evaluated by a series of 130 studies focusing on tracers including CH 4 (Pison et al., 2009;Locatelli et al., 2015;Cressot et al., 2014), HCHO (Fortems-Cheiney et al., 2012), CO (Yin et al., 2015;Zheng et al., 2019), and CO 2 (Chevallier et al., 2005(Chevallier et al., , 2010. We use a recently developed version that has the capacity to assimilate observations of the major tracers in the CH 4 oxidation chain, namely CH 4 -HCHO-CO, with OH being their common sink path, to optimize the sources and sinks for all these species simultaneously.
Here, we use a simplified chemistry scheme that assumes methane being oxidized into formaldehyde in a single step. We 135 expect this simplification to have a relatively small impact on the inverse results of methane, given that all pathways of methane oxidation result in formaldehyde as an intermediate product. Besides, HCHO production from non-methane VOC oxidation is simulated upstream with a full-chemistry model, so that the correction on OH from the inversion will not directly feedback to the VOC oxidation. This should not be an issue as we optimize the production of HCHO instead of VOC emissions, but the impact of VOC on OH recycling is not accounted for. Future studies using a full chemistry scheme to optimize methane and 140 OH simultaneously would be helpful to diagnose potential impacts of this simplification on the derived methane lifetime.
The CTM version we use here has a horizontal resolution of 1.875°×3.75°(latitude, longitude) and a vertical resolution of 39 eta levels. Atmospheric transport is guided by the ERA-Interim meteorological reanalyses (Dee et al., 2011) to represent changes in the dynamics. Given observational information of the three species, the system optimizes the following quantities at each grid cell at a weekly resolution: (i) surface emissions of CH 4 , (ii) surface emissions of CO, (iii) scaling factors for the sum 145 of HCHO emissions and its chemical production from hydrocarbon oxidation, (iv) scaling factors of the OH concentration, and (v) the initial state of all the four species CH 4 , HCHO, CO, and OH. The assimilation is performed continuously for the entire study period to avoid errors in temporal segmentation. The minimization of the cost function is solved iteratively until it reaches a reduction of 99% in the gradient of the cost function or a minimum of 45 iterations. The reduced chi-squared (J divided by the number of observations) is about 0.5, which is much lower than 1 because of observation error inflation to 150 compensate for the fact that we do not account for observation error correlations following findings of Chevallier (2007).  Table S2, and their spatial distributions are shown in Fig. S3. Note that soil deposition is treated as negative fluxes from the land to the atmosphere, and the emissions reported in this study are hence the net methane fluxes from the land to the atmosphere. The Gaussian uncertainty is set as 70% and 100% respectively for gridded CH 4 and CO emissions, 160 whereas 200% for chemical HCHO productions and 20% for OH. Those errors are chosen empirically given the spreads across different bottom-up estimates. The a priori spatial error correlations are defined by an e-folding length of 500 km over the land and 1000 km over the ocean. Temporal error correlations are defined by an e-folding length of 2 weeks. We do not account for error correlations across species.
We include two alternative prior estimates for the OH concentration: one based on a full chemistry simulation by the 165 model LMDZ-INCA (Hauglustaine, 2004), noted as INCA-OH hereafter, and one from the TransCom model intercomparison experiment for methane and related species (Patra et al., 2011), noted as TransCom-OH. The two OH fields have contrasting 3D distributions that could help to evaluate the impact of OH distributions on the resultant methane fluxes (Yin et al., 2015).
In particular, the two OH fields have different Northern to Southern hemisphere ratios: ∼1.2 for INCA and ∼1 for TransCom.
Similar to the prior estimates of the emissions, there are no interannual variations in the prior estimates of OH fields. Note that 170 for the case of assimilating surface observations (S1), the spatial error correlation of OH are set to 1 within 6 latitudinal zones (90-60S, 60-30S, 30-0S, 0-30N, 30-60N, 60-90N) and 0 across them, i.e. the zonal mean OH is optimized instead of per grid cell given limited observational constraints.
In summary, we include six inversions here with three different observational constraints and each pairing with two different prior estimates of global OH distributions (Table S1).

Information Content Analysis
While the variational inverse system has the advantage of optimizing large state vectors of fluxes for multiple species at high spatial and temporal resolutions, it is computationally too expensive to calculate the error covariances of posterior fluxes.
Hence, we perform additional analytical inversions for aggregated source regions to estimate information content of available methane observations on regional emissions and posterior error covariances. In this configuration, the state vector x becomes 180 monthly regional emissions from 18 regions across the globe (regional mask shown in Fig. 8) plus a background term and the impacts of changes in OH are not accounted for. The transport model and observation operator K, relating each element of x to observable quantities y can be numerically simulated. Using x a to represent the prior, S a and S ε to represent the error covariance matrices of the state vector x and of the observation vector y, the posterior solution is expressed aŝ Here, G represents the gain matrix that describes the sensitivity of the fluxes to observations, i.e. G=∂x/∂y. The error covariance matrixŜ ofx can be derived aŝ The ability of an observational system to constrain the true value of the state vector can be represented by the sensitivity of the posterior solutionx to the true state x, commonly termed as the averaging kernel matrix A=∂x/∂x, as the product of the gain matrix G and the Jacobian matrix K=∂y/∂x, so that A=GK (Rodgers, 2000). This complementary analysis provides us important estimates of how much information content can the surface and satellite methane observations provide on regional methane emission changes. In general, the observed global average methane growth rate is well captured by the posterior model states both at the surface sites and through the total column, irrespective of which data being assimilated ( Fig. 2b and c). Sampled from the same ensemble of posterior model states, the surface growth rates show a sharp increase in 2014 (Fig. 2b), whereas more gradual 200 increase is found in the column average (Fig. 2c). The agreement across different inversions demonstrates that differences in the temporal variations of the growth rates seen by surface and GOSAT observations are primarily due to 3-dimensional sampling differences rather than by some inconsistency between those two types of observations. This contrast suggests that the sharp increase in the surface methane growth rate in 2014 could have been amplified by sampling effect of the sparse surface network as also shown by a longer record . Surface in-situ observations with high precision and   occurred in 2012 and the highest in 2017. Current global CH 4 emissions are thus at a maximum level within the past million years, with high growth rates similar to the 1980s, during which the total methane sink was, however, not as high as today due to a lower CH 4 burden.

Variations attributed to OH
Changes in the inferred OH concentrations are less than 1% at the global scale, with a small increase during 2010-2014 followed by a small decline thereafter (Fig. 3). The resulting decrease in OH since 2014, albeit small in magnitude, occurs in both the surface-driven (S1) and satellite-driven (S3) inversions, most notably in the Southern Hemisphere (Fig. S4). Inflating the prior OH uncertainty up to ±50% at each model grid only results in larger scaling factors on the OH distribution but not 230 higher temporal variations. The resultant small interannual variations in the posterior OH field is in line with a modeling study that showed a high OH recycling probability and hence a weak sensitivity to emission perturbations (Lelieveld et al., 2016).
Some atmospheric chemistry models simulate a slightly larger year-to-year variability (1-4%) (Holmes et al., 2013;Turner et al., 2018), while recent data-constrained estimates using observed ozone columns, water vapor, methane, model-simulated NOx, and Hadley cell width suggest a relatively stable OH level over the past several decades (Nicely et al., 2018). In addition, 235 compared to earlier box model studies that infer around 5% OH IAV from methyl chloroform (MCF) and δ 13 CH 4 observations (Turner et al., 2017;Rigby et al., 2017), a recent box model study that accounts for model biases related to tracer specific dynamics suggest a smaller IAV in OH (Naus et al., 2019).
Recent GOSAT inverse studies explored optimizing gridded annual anthropogenic methane emissions and associated trends, regional monthly wetland emissions, and global (or hemispheric) annual OH concentration with an analytical inversion 240 scheme, where it is possible to compute the full posterior error covariance matrix (Maasakkers et al., 2019;Zhang et al., 2021).
The results suggest a strong negative error correlation between global anthropogenic emissions and methane lifetime (r=-0.8), moderate correlations between wetland emissions and methane lifetime (r=-0.4), and between OH trend and wetland or anthropogenic emission trends (r=-0.6) (Zhang et al., 2021). Hence, assimilating GOSAT data alone, the inversion has limited information to separate the sources and the sinks. With our multi-species variational inverse system, it is computational too 245 costly to estimate the posterior error covariances using a Monte Carlo approach. Given the strong error correlations between the source and sink terms identified by Zhang et al. (2021), we cannot rule out the possibility that numerically it might be easier for the optimization system to adjust surface emissions of the three species to fit the observations rather than modifying OH to adjust the sink terms in the absence of a mechanistic chemical feedback in the chemical transport model. The feedback effects are mostly tested using box models at the current stage (Prather, 1994;Nguyen et al., 2020), future studies accounting 250 for these effects in a 3-D inversion would be helpful to diagnose its impacts on estimated changes in methane lifetimes. Differences of zonal flux distributions are noted across versions, most notably between surface and satellite data constraints. For the same observational constraints, inversions using INCA OH fields result in higher Northern hemisphere emissions compared to the cases using TransCom OH fields due to a higher North-to-South Hemispheric OH ratio of the former.
Compared to the results assimilating surface observations (S1), assimilating GOSAT X CH4 retrievals (S2 & S3) allocates  Northern mid-high latitudes against surface observations (Fig. S7), while the biases turn positive gradually toward the tropics and the southern hemisphere ( Figure S16). However, no latitude-dependent biases are found between GOSAT-assimilated posterior model states (S2 & S3) against TCCON total column measurements, and the magnitude of remaining biases are in line with GOSAT data validation (Parker et al., 2015). Yet S1 show similar model bias structure against TCCON as compared to GOSAT X CH4 (Fig. S7), suggesting discrepancies in the vertical distribution of methane concentrations between the model 275 and the satellite retrievals. Such a bias pattern between model and surface or GOSAT data has been identified by previous inverse studies (Alexe et al., 2015;Turner et al., 2015;Miller et al., 2019;Maasakkers et al., 2019), which is likely related to biases in the model representation of the stratosphere. An empirical bias-correction on the GOSAT data so that the assimilated model states also agree with surface observations are typically applied by some studies. Here, since we focus on the IAVs of the posterior fluxes where systematic biases do not impact such results, we did not apply an empirical bias correction to the 280 GOSAT data. Future studies to correct those biases with mechanistic understandings will be very valuable.
13 4.2 Information content of observations on regional fluxes To assess the extent to which the surface and satellite observations can inform us about changes of methane fluxes in distinct regions, we conducted an information content analysis for a total of 18 regions (see Section 2.2.3). The regional mask following the convention of the Global Carbon Project (Saunois et al., 2019) is shown in Fig. 8. Note that this analysis assumes all 285 atmospheric methane changes are resulted from surface flux changes and hence does not account for potential contributions from changes in OH or other sink processes. The results suggest that, in most cases, GOSAT data provide more constraints on regional emissions than the surface observations (Fig. 6). This is particularly obvious in the tropics and subtropics, including Amazon, Eastern Brazil, Southern South America, Northern Africa, Tropical Africa, Southern Africa, Mideast, India, and Southeast Asia. This is because fewer surface sites exist in those regions but satellite data have a better coverage. Consequently, 290 the posterior errors in the optimized emissions constrained by satellite data are less correlated across different regions compared to the case with surface data constraints only (Fig. S8). The error covariances suggest that the surface observations alone, mostly located in the background boundary layer, is insufficient to separate tropical emissions from the three continents -South America, Africa, and Asia. In contrast, the cross-error terms in the GOSAT inversion are much smaller, suggesting that to a large extent emissions from different regions can be individually constrained by these X CH4 observations. 295 4.3 Regional emission changes Looking into regional emission changes, the differences in the posterior CH 4 emissions between the last and the first four years of our study period (2014-2017 vs. 2010-2013) are shown in Fig. 7, while regional masks of the 18 sub-regions and regional annual emission anomalies are shown in Fig. 8. The most substantial increases between the two periods occurred in Amazon, China, and Tropical Africa, by 4.2±1.2, 3.7±1.0, and 2.1±0.8 Tg yr −1 respectively (Fig. 7). Changes in the three regions 300 amounts to nearly 60% of the global emission increase. This increase does not necessarily imply linear trends in emissions as there are considerable interannual variations in the derived emissions (Fig.8), in particular, the first period includes a La Niña year 2011 during which high wetland emissions are reported  and the latter period includes a strong El Nino year 2015 during which large fire emissions are reported (Yin et al., 2016;Worden et al., 2017). While all the six inversions agree on such a regional pattern, the multi-tracer versions (S3), that optimize OH concentrations simultaneously 305 with the surface methane fluxes, infer smaller CH 4 emission increases compared to the version assimilating GOSAT alone (S2). This difference could stem from adapting the regional mean OH level that converts the same concentration change to different emission changes. In addition, differences between S2 and S3 could result from the variational inversion reaching different approximations of the cost function minimum.
To gain further understanding of observed changes in regional CH 4 emissions, we attribute our inversion emission 310 anomaly estimates into the following categories, based on our prior bottom-up emission inventory: fossil fuel (oil, gas, coal mining, industry, residential, transport, and geological), waste (landfills and wastewater), agriculture (enteric fermentation, Figure 6. Averaging kernels (AK) of regional emissions to observations over that region during each month. Diagonal terms of the AK matrix are shown using data of the year 2010 as an example. manure management, and rice cultivation), wetlands (including inland water), and fire (including biofuel). We acknowledge the fact that this prior information has significant uncertainties as evidenced by the large spread across different bottom-up inventories (Saunois et al., 2016). The proportion of the different sectors remains unchanged in each grid cell throughout all 315 years, except for fire, because we use a climatological estimates for prior emissions. Our emission attribution thus reflects a likelihood of contributing processes at a given location and season, which is larger, and most useful, in regions where emissions are predominately contributed by a specific sector (Fig. S10 & S11).
For the Amazon, wetlands are the major contributor to CH 4 emissions according to the bottom-up emission inventories, and hence our identified source for the increase, showing an average trend of 0.8±0.1 Tg yr −2 over the eight study years with 320 shorter-term interannual variations (Fig. 8). Fire emissions from this region were high during the 2010 drought but did not rise significantly in the recent 2015 El Nino, which is in agreement with previous fire emission estimates based on CO and CO 2 (Gatti et al., 2014;Liu et al., 2017).  tion of all versions. The regional mask is shown in Fig. 8. matter, and hence depend on organic carbon inputs and inundation areas, and exponentially on temperature (Whalen, 2005).
Consistent behaviors between the time and locations of anomalies in the GOSAT X CH4 and changes in wetland extent have been documented with the focus on seasonally flooded wetlands . An intensification of Amazon flooding extremes has been documented based on water levels in the Amazon river, with anomalously high flood levels and long flood 330 durations since 2012 (Barichivich et al., 2018), which could result in higher wetland CH 4 emissions.
For the other tropical regions, significant increases are also attributed to wetland emissions, in particular to Tropical Africa (1.5±0.7 Tg yr −1 , Fig. 8). The increasing tropical Africa wetland emissions is consistent with a recent regional inversion using GOSAT data at a high spatial resolution of 0.5°×0.625°, which find a positive trend of 1. South America (0.1±0.1 Tg yr −1 ). However, other emission sources also play a significant role in these regions, in particular agricultural emissions . Thus future studies with additional constraints on wetland emissions are needed to better quantify wetland-related changes. Only in Southeast Asia, the major contribution to different CH 4 emissions between the two periods is from fire associated with the strong El Niño in 2015 (Yin et al., 2016;Liu et al., 2017). No significant increases 340 are noted for India, consistent with a previous regional study focusing on the 2010-2015 period .  (Zhang et al., 2021). The numbers are comparable given the differences in the inverse set ups and the chemical transport models being used.

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Russia also contributed significant increase in CH 4 emissions, by 1.7±0.7 Tg yr −1 between 2010-13 and 2014-17 (Fig.   7), possibly from both fossil fuel extraction in Northern Russia and extensive peatland areas (Fig. 8). The surface-driven and satellite-driven inversions identify slightly different source regions for the rise (Fig. 5). The surface-driven inversions attribute most of the increases to the European part of Russia where anthropogenic emission dominate, whereas the satellite-driven inversions attribute more changes to the West Siberia plain where more wetlands are located (Terentieva et al., 2016). As there 355 are both fossil fuel and wetland sources in the west Siberia plain (Fig. S10), further information is needed to disentangle relative contributions between anthropogenic and natural wetland sources. For the other extratropical regions showing significant CH 4 emission increases, the increase in Canada (1.1±0.4 Tg yr −1 ) was mostly attributed to wetlands (Fig. 8), with interannual variations consistent with previous regional inversions (Sheng et al., 2018). Relatively small increase in the US is found after 2014 (0.7±0.2 Tg yr −1 ) with nearly flat emissions before, which is consistent with previous studies finding no trend over US 360 before 2012 (Saunois et al., 2017;Bruhwiler et al., 2017).
Small increases in the US (0.7±0.2 Tg yr −1 ) occurred after 2014 with considerable overlapping contributions from different sectors in the prior, preventing a robust sectoral breakdown (Fig. 8). Relatively small increase is found after 2014 with flat emissions before, which is consistent with previous studies finding no trend over US before 2012 (Saunois et al., 2017, Bruhwiler et al., 2017 365 Relying on the prior distribution to approximate possible contributions from wetlands in the mid-high latitudes, the in-  (Fig. S12a). The general increase in wetland CH 4 fluxes is in line with observed atmospheric δ 13 CH 4 that shows a general negative trend at all latitudes (Fig. S12b), as biogenic sources like wetlands are more δ 13 CH 4 depleted than the other ones (Sherwood et al., 2017). Besides, anthropogenic emissions from agriculture and waste management are also associated with a biogenic δ 13 CH 4 signature.

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Our ensemble of inversions assimilating surface or satellite CH 4 observations, as well as chemically-related tracers to partly constrain the OH sink, consistently suggests that the recent acceleration in CH 4 growth rate from 2010 to 2017 is most likely induced by increases in surface emissions. The derived global emissions point to an unprecedented new maximum in global total methane emissions. The most substantial increases during the eight study years come from the tropics and East Asia.
Given our prior knowledge on the distribution of different CH 4 sources, natural wetland emissions show the largest increase 380 with dominant contributions from the tropics. Such an increase would result in potential positive feedback to climate warming . The second-largest increase comes from anthropogenic emissions in China, despite recent government regulations . The continuation of existing surface CH 4 and δ 13 CH 4 observations and GOSAT/GOSAT-2 X CH4 retrievals, the newly available TROPOspheric Monitoring Instrument (TROPOMI) observations with frequent global mapping capability (Hu et al., 2018), and the coming of new methane space missions such as the MEthane Remote sensing 385 Lidar missioN (MERLIN) (Bousquet et al., 2018) will bring further insight into regional methane budget changes and their climate sensitivity. Here, we tested the consistency of using different observational constraints and different prior OH distributions. The sensitivity of prior emission estimates and associated uncertainty characteristics, and transport model errors could be further explored by future model intercomparison studies (?). Future studies using spatial-temporal variations in the observed atmospheric δ 13 CH 4 and spatially resolved isotopic source signatures (Ganesan et al., 2018) will provide further con-390 straints on the source attribution. At the same time, a process-based understanding of the wetland CH 4 emissions and effective anthropogenic emission regulation measures are urgently needed to meet future climate mitigation goals.