ble 3), which is similar to the one reported previously in Saunois et al. (2020) from the CCMI model (553 [476–677] Tg CH₄ yr⁻¹) and still slightly higher than the one from the ACCMIP models (528 [454–617] Tg CH₄ yr⁻¹) reported in Kirschke et al. (2013) and Saunois et al. (2016).

For the recent 2010–2019 decade, we report a climatological value based on only five models that contributed to CMIP6 runs (historical run followed by SSP3-7.0 projections starting in 2015, Collins et al., 20217) to acknowledge the impact of the rise in atmospheric methane on the methane chemical sink. Hence, for 2010–2019, we report the climatological value of 563 [462–663] Tg CH₄ yr⁻¹ (Table 3).

### 3.3.2 Stratospheric loss

In the stratosphere, CH₄ is lost through reactions with excited atomic oxygen O(¹D), atomic chlorine (Cl), atomic fluorine (F), and OH (Brasseur and Solomon, 2005; le Texier et al., 1988). Uncertainties in the chemical loss of stratospheric CH₄ are large, due to uncertain interannual variability in stratospheric transport (Zhang et al., 2023) as well as its chemical interactions and feedbacks with stratospheric O₃ (Morgenstern et al., 2018). Particularly, the fraction of stratospheric loss due to the different oxidants is still uncertain, with possibly 20%–35% due to halons, about 25% due to O(¹D) mostly in the high stratosphere, and the rest due to stratospheric OH (McCarthy et al., 2003).

In this study, six chemistry–climate models that contributed to CMIP6 modelling activities (Table S5) provided estimates of CH₄ chemical loss, including reactions with OH, O(¹D), and Cl; CH₄ photolysis is also included but occurs only above the stratosphere. Considering a 200 hPa tropopause height, these six CMIP6 simulations suggest an estimate of 34 [10–51] Tg CH₄ yr⁻¹ for the CH₄ stratospheric sink for the 2000–2009 decade (Table S5), similar to the value derived from the previous CCMI activity reported in Saunois et al. (2020) (31 [12–41] Tg CH₄ yr⁻¹). The lowest estimate provided by a model (10 Tg CH₄ yr⁻¹) is quite unrealistic and would yield a methane stratospheric lifetime of several hundreds of years. As a result, this outlier is excluded, and we prefer to report a mean of 39 Tg CH₄ yr⁻¹ associated with a range of [27–51] for 2000–2009.

For 2010–2019, we report here a climatological range of 28–43 Tg CH₄ yr⁻¹ associated with a mean value of 37 Tg CH₄ yr⁻¹ (Table 3) based on five models that contributed to CMIP6 runs (historic followed by SSP3-7.0 projections starting in 2015; Table S5).

### 3.3.3 Tropospheric reaction with Cl

Halogen atoms can also contribute to the oxidation of CH₄ in the troposphere. Allan et al. (2005) measured mixing ratios of methane and δ¹³C–CH₄ at two stations in the Southern Hemisphere from 1991 to 2003 and found that the apparent kinetic isotope effect (KIE) of the atmospheric CH₄

sink was significantly larger than that explained by OH alone. A seasonally varying sink due to Cl in the marine boundary layer of between 13 and 37 Tg CH₄ yr⁻¹ was proposed as the explanatory mechanism (Allan et al., 2007; Platt et al., 2004). This sink was estimated to occur mainly over coastal and marine regions, where sodium chloride (NaCl) from evaporated droplets of seawater react with NO₂ to eventually form Cl₂, which then UV-dissociates to Cl. However significant production of nitryl chloride (CINO₂) at continental sites has been recently reported (Riedel et al., 2014) and suggests the broader presence of Cl, which in turn would expand the significance of the Cl sink in the troposphere. Recently, Hossaini et al. (2016), Sherwen et al. (2016), and Wang et al. (2019b, 2021b) have made significant improvements in tropospheric chemistry modelling, and they suggest a contribution to the total oxidation of 2.6%, 2%, 1%, and 0.8% respectively. These values correspond to a tropospheric CH₄ loss of around 12–13 Tg CH₄ yr⁻¹, 9 Tg CH₄ yr⁻¹, 5 Tg yr⁻¹, and 3 Tg CH₄ yr⁻¹ respectively, much lower than the first estimates by Allan et al. (2007). The recent work of Wang et al. (2021b) is the most comprehensive modelling study and based upon Sherwen et al. (2016) and Wang et al. (2019b). Both the KIE approach and chemistry–transport model simulations carry uncertainties (extrapolations based on only a few sites and use of indirect measurements, for the former and missing sources, coarse resolution, underestimation of some anthropogenic sources for the latter). However, Gromov et al. (2018) found that Cl can contribute only 0.23% the tropospheric sink of CH₄ (about 1 Tg CH₄ yr⁻¹) in order to balance the global ¹³C(CO) budget (see their Table S1). While tropospheric Cl has a marginal impact on the total CH₄ sink (few percents), it influences more significantly the atmospheric isotopic δ¹³C–CH₄ signal, and improved estimates of the tropospheric Cl amount should be used for isotopic CH₄ modelling studies (Strode et al., 2020; Thanwerdas et al., 2022b).

Each recent Cl estimate suggests a reduced contribution to the methane loss than previously reported by Allan et al. (2007). As a result, we suggest here to use the mean, minimum, and maximum of the last five estimates published since 2016, leading to a climatological value of 6 [1–13] Tg CH₄ yr⁻¹ (Table 3), thus reducing both the magnitude and the uncertainty range compared to Saunois et al. (2020).

### 3.3.4 Soil uptake

Unsaturated oxic soils are sinks of atmospheric CH₄ due to the presence of methanotrophic bacteria, which consume CH₄ as a source of energy. Dutaur and Verchot (2007) conducted a comprehensive meta-analysis of field measurements of CH₄ uptake spanning a variety of ecosystems. Extrapolating to the global scale, they reported a range of 36 ± 23 Tg CH₄ yr⁻¹ but also showed that stratifying the results by climatic zone, ecosystem, and soil type led to a narrower range (and lower mean estimate)
of 22 ± 12 Tg CH₄ yr⁻¹. Modelling studies, employing me-
teorological data as external forcing, have also produced
a considerable range of estimates. Using a soil-depth-
averaged formulation based on Fick's law with parame-
terisations for diffusion and biological oxidation of CH₄,
Ridgwell et al. (1999) estimated the global sink strength at
38 Tg CH₄ yr⁻¹, with a range 20–51 Tg CH₄ yr⁻¹ reflecting
the model structural uncertainty in the base oxidation pa-
rameter. Curry (2007) improved on the latter by employing
an exact solution of the one-dimensional diffusion–reaction
equation in the near-surface soil layer (i.e. exponential de-
crease in CH₄ concentration below the surface), a land sur-
face hydrology model, and calibration of the oxidation rate
to field measurements. This resulted in a global estimate of
28 Tg CH₄ yr⁻¹ (9–47 Tg CH₄ yr⁻¹), the result reported by
Zhuang et al. (2013), Kirschke et al. (2013), and Saunois et
al. (2016). Ito and Inatomi (2012) used an ensemble method-
ology to explore the variation in estimates produced by these
parameterisations and others, which spanned the range 25–
35 Tg CH₄ yr⁻¹. For the period 2000–2020, as part of the
wetland emissions modelling activity, JSBACH (Kleinen et
al., 2020) and VISIT (Ito and Inatomi, 2012) models com-
pute a global CH₄ soil uptake to 18 and 35 Tg CH₄ yr⁻¹ re-
spectively. Murguia-Flores et al. (2018) further refined the
Curry (2007) model's structural and parametric represen-
tations of key drivers of soil methanotrophy, demonstrat-
ing good agreement with the observed latitudinal distribu-
tion of soil uptake (Dutaur and Verchot, 2007). Their model
(MeMo) simulates a CH₄ soil sink of 37.5 Tg CH₄ yr⁻¹
for the period 2010–2019 (Fig. S4), compared to 39.5 and
31.3 Tg CH₄ yr⁻¹ using the Ridgwell et al. (1999) and Curry
(2007) parameterisations respectively under the same me-
teorological forcing, run specifically for this study. For the
2000s period, the simulations estimate the soil uptake at 30.4,
36.7, and 38.3 Tg CH₄ yr⁻¹ based on the parameterisation of
Curry, MeMo, and Ridgwell respectively. As part of a more
comprehensive model accounting for a range of CH₄ sources
and sinks, Tian et al. (2010, 2015, 2016) computed vertically
averaged CH₄ soil uptake including the additional mecha-
nisms of aqueous diffusion and plant-mediated (aerenchyma)
transport, arriving at the estimate 30 ± 19 Tg CH₄ yr⁻¹ (Tian
et al., 2016) for the 2000s. The still more comprehensive bio-
geochemical model of Riley et al. (2011) included vertically
resolved representations of the same processes considered by
Tian et al. (2016), in addition to grid cell fractional inunda-
tion and, importantly, the joint limitation of uptake by both
CH₄ and O₂ availability in the soil column. Riley et al. (2011)
estimated a global CH₄ soil sink of 31 Tg CH₄ yr⁻¹ with a
structural uncertainty of 15–38 Tg CH₄ yr⁻¹ (a higher upper
limit resulted from an elevated gas diffusivity to mimic con-
nective transport; as this is not usually considered, we adopt
the lower upper bound associated with no limitation of up-
take at low soil moisture). A model of this degree of com-
plexity is required to explicitly simulate situations where the
soil water content increases enough to inhibit the diffusion

of oxygen, and the soil becomes a methane source (Lohila
et al., 2016). This transition can be rapid, thus creating areas
(for example, seasonal wetlands) that can be either a source
or a sink of methane depending on the season.

The previous Curry (2007) estimate can be revised up-
ward slightly based on subsequent work and the increase in
CH₄ concentration since that time. Indeed, Murguia-Flores
et al. (2021) estimated that the global soil uptake doubled
between 1900 and 2015 and could further increase due to
enhanced diffusion of CH₄ into soil as a result of increases
in atmospheric CH₄ mole fraction. Further investigation of
the soil uptake is required to better constrain this process
at the global scale, while it is highly dependent on local-
scale microbial activity and environmental conditions (e.g.
D’Imperio et al., 2023; Fest et al., 2017).

Considering the latest estimates (based on VISIT, JS-
BACH, and MeMo models; Table S6 in the Supplement), we
report here a mean estimate of 31 [17–39] Tg CH₄ yr⁻¹ for
2000–2009 and 32 [18–40] for 2010–2019 Tg CH₄ yr⁻¹ (Ta-
ble 3).

3.3.5 CH₄ lifetime

The atmospheric lifetime of a given gas in steady state may
be defined as the global atmospheric burden (Tg) divided by
the total sink (Tg yr⁻¹) (IPCC, 2001). This value is different
from what is called perturbation lifetime. Perturbation life-
time is used to determine how a one-time pulse emission may
decay as a function of time as needed for the calculation of
global warming potentials (GWPs) and as a result is related
to a theoretical concept. For CH₄, the corresponding pertur-
bation lifetime that should be used in the GWP calculation
is 11.8 ± 1.8 years (Forster et al., 2021). In this section, we
discuss the global atmospheric lifetime (also called “burden
lifetime” or “turnover lifetime”) that characterises the time
required to turn over the global atmospheric burden and de-
fined as the burden divided by the removal flux.

Global models provide an estimate of the loss of the gas
due to individual sinks, which can then be used to derive
lifetime due to a specific sink. For example, the tropospheric
lifetime of CH₄ is determined as the global atmospheric CH₄
burden divided by the loss from OH oxidation in the tro-
posphere, sometimes called “chemical lifetime”. The total
lifetime of CH₄ corresponds to the global burden divided
by the total loss including tropospheric loss from OH oxi-
dation, stratospheric chemistry, and soil uptake. The CCMI
(Plummer et al., 2021) and CMIP6 (Collins et al., 2017)
runs estimate the tropospheric methane lifetime at about
9.2 years (average over years 2000–2009), with a range of
7.5–11 years (see Table S5). This range agrees with pre-
vious values found in ACCMIP and CCMI (9.3 [7.1–10.6]
years; Voulgarakis et al., 2013, 9 [7.2–10.1] years; Saunois
et al., 2020). Adding 31 Tg to account for the soil uptake
to the total chemical loss of the CMIP6 and CCMI mod-
els, we derive a total CH₄ lifetime of 8.2 years (average over
2000–2009 with a range of 6.8–9.7 years). The lifetime calculated over 2010–2019 based on CMIP6 simulations is similar (Table S5). These updated model estimates of total CH4 lifetime agree with the previous estimates from ACCMIP (8.2 [6.4–9.2] years for year 2000; Voulgarakis et al., 2013) and Saunois et al. (2020)-based CCMI models. Reducing the large spread in CH4 lifetime (between models, and between models and observation-based estimates) would (1) bring an improved constraint on global total methane emissions and (2) ensure an accurate forecast of future climate.

# 4 Atmospheric observations and top-down inversions

## 4.1 Atmospheric observations

Systematic atmospheric CH4 observations began in 1978 (Blake et al., 1982) with infrequent measurements from discrete air samples collected in the Pacific at a range of latitudes from 67°N to 53°S. Because most of these air samples were from well-mixed oceanic air masses and the measurement technique was precise and accurate, they were sufficient to establish an increasing trend and the first indication of the latitudinal gradient of methane. Spatial and temporal coverage was greatly improved soon after (Blake and Rowland, 1986) with the addition of the Earth System Research Laboratory from the US National Oceanic and Atmospheric Administration (NOAA/GML) flask network (Steele et al., 1987; Lan et al., 2024; Fig. 1), and the Advanced Global Atmospheric Gases Experiment (AGAGE) (Cunnold et al., 2002; Prinn et al., 2018), the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Francey et al., 1999), the University of California Irvine (UCI, Simpson et al., 2012), and in situ and flask measurements from regional networks, such as ICOS (Integrated Carbon Observation System) in Europe (https://www.icos-ri.eu/, last access: 1 April 2025). The combined data sets provide the longest time series of globally averaged CH4 abundances. Since the early 2000s, CH4 column-averaged mole fractions have been retrieved through passive remote sensing from space (Buchwitz et al., 2005a, b; Butz et al., 2011; Crevoisier et al., 2009; Frankenberg et al., 2005; Hu et al., 2018). Ground-based Fourier transform infrared (FTIR) measurements at fixed locations also provide time-resolved CH4 column observations during daylight hours and a validation data set against which to evaluate the satellite measurements such as the Total Carbon Column Observing Network (TCCON) network (e.g. Pollard et al., 2017; Wunch et al., 2011) or the Network for Detection of Atmospheric Composition Change (NDACC) (e.g. Bader et al., 2017).

In this budget, in situ observations from the different networks were used in the top-down atmospheric inversions to estimate CH4 sources and sinks over the period 2000–2020. Satellite observations from the TANSO/FTS instrument on board the satellite GOSAT were used to estimate

CH4 sources and sinks over the period 2010–2020. Other atmospheric data (FTIR, airborne measurements, AirCore, isotopic measurements, etc.) have been used for validation by some groups, but not specifically in this study. However, further information is provided in Tables S7, S8, S9, S10, and S11 and a more comprehensive validation of the inversions is planned to use some of these data.

### 4.1.1 In situ CH4 observations and atmospheric growth rate at the surface

We use globally averaged CH4 mole fractions at the Earth's surface from the four observational networks (NOAA/GML, AGAGE, CSIRO, and UCI). The data are archived at the World Data Centre for Greenhouse Gases (WD-CGG) of the WMO Global Atmospheric Watch (WMO-GAW) programme (https://gaw.kishou.go.jp/, last access: 1 April 2025), including measurements from other sites that are not operated as part of the four networks. The CH4 in situ monitoring network has grown significantly over the last decade due to the emergence of laser diode spectrometers, which are robust and accurate enough to allow deployments with low maintenance, enabling the development of denser networks in developed countries (Stanley et al., 2018; Yver Kwok et al., 2015) and new stations in remote environments (Bian et al., 2015; Nisbet et al., 2019).

The networks differ in their sampling strategies, including the frequency of observations, spatial distribution, and methods of calculating globally averaged CH4 mole fractions. Details are given in the supplementary material of Kirschke et al. (2013). The global average values of CH4 abundances at Earth's surface presented in Fig. 1 are computed using long-term measurements from background conditions with minimal influence from immediate emissions. All measurements are calibrated against gas standards either on the current WMO reference scale or on independent scales with well-estimate differences from the WMO scale. The current WMO reference scale, maintained by NOAA/ESRL, WMO-X2004A (Dlugokencky et al., 2005), was updated in July 2015. NOAA and CSIRO global means are on this scale. AGAGE uses an independent standard scale (based on work by Tohoku University (Aoki et al., 1992) and maintained at Scripps Institution of Oceanography (SIO)), but direct comparisons of standards and indirect comparisons of atmospheric measurements show that differences are well below 5 ppb (Tans and Zwellberg, 2014; Vardag et al., 2014), and the TU-1987 scale used for AGAGE measurements is only a 0.5 ppb difference from WMO-X2004A at 1900 ppb level. UCI uses another independent scale that was established in 1978 and is traceable to NIST (Flores et al., 2015; Simpson et al., 2012) but has not been included in standard exchanges with other networks, so differences with the other networks cannot be quantitatively defined. Additional experimental details are presented in the supplementary material of Kirschke et al. (2013) and references therein.
The globally averaged CH₄ (shown in Fig. 1a) and its growth rate (derivative of the deseasonalised trend curve; shown in Fig. 1b) through to 2022 are plotted for the four measurement programmes using a procedure of signal decomposition described in Thoning et al. (1989). We define the annual G_ATM as the increase in the atmospheric concentrations from 1 January in one year to 1 January in the next year. Agreement among the four networks is good for the global growth rate, especially since ~1990. The large differences observed mainly before 1990 probably reflect the different spatial coverage of each network. The long-term behaviour of globally averaged atmospheric CH₄ shows a positive growth rate (defined as the derivative of the deseasonalised mixing ratio) that slows down from the early 1980s through 1998, a near-stabilisation of CH₄ concentrations from 1999 to 2006, and a renewed period with positive persistent overall accelerating growth rates since 2007, slightly larger after 2014.

From 1999 to 2006, the annual increase in atmospheric CH₄ was remarkably small at 0.6 ± 0.1 ppb yr⁻¹. After 2006, the atmospheric growth rate increased to a level similar to that of the mid-1990s (~5 ppb yr⁻¹) and for 2014 and 2015 even to that of the 1980s (> 10 ppb yr⁻¹). In the two recent years 2020 and 2021, the highest growth rates of 15 ppb yr⁻¹ and 18 ppb yr⁻¹ (see Sect. 6) were unprecedented starting in the 1980s. On decadal timescales, the annual increase is on average 2.2 ± 0.3 ppb yr⁻¹ for 2000–2009, 7.6 ± 0.3 ppb yr⁻¹ for 2010–2019, and 15.2 ± 0.4 ppb yr⁻¹ for the year 2020 (Table 3). Both climate variability and anthropogenic emission changes are responsible for variations in atmospheric CH₄ growth rates. Indeed, climate variations such as El Niño–Southern Oscillation induce changes in emissions such as biomass burning or wetland emission but also impact OH oxidation (e.g. Rowlinson et al., 2019; Zhao et al., 2020b; Peng et al., 2022).

### 4.1.2 Satellite data of column average CH₄

In this budget, we use satellite data from the JAXA satellite Greenhouse Gases Observing SATellite (GOSAT) launched in January 2009 (Butz et al., 2011; Morino et al., 2011) containing the TANSO-FTS instrument, which observes in the shortwave infrared (SWIR). Different retrievals of CH₄ based on TANSO-FTS/GOSAT products are made available to the community: from NIES (Yoshida et al., 2013), from SRON (Schepers et al., 2012), and from the University of Leicester (Parker et al., 2020; Parker and Boesch, 2020). The three retrievals are used by the top-down systems (Tables 4 and S6). Although GOSAT retrievals still show significant unexplained biases and limited sampling in cloud covered regions and in the high-latitude winter, they represent an important improvement compared to the first satellite measuring CH₄ from space, SCIAMACHY (Scanning Imaging Absorption spectrometer for Atmospheric CartographY), both

for random and systematic observation errors (see Table S2 of Buchwitz et al., 2016).

Here, as in Saunois et al. (2020), only inversions using GOSAT retrievals are used.

## 4.2 Top-down inversions used in the budget

An atmospheric inversion is the optimal combination of atmospheric observations, of a model of atmospheric transport and chemistry, of a prior estimate of CH₄ sources and sinks, and of their uncertainties, to provide improved estimates of the sources and sinks and their uncertainty. The theoretical principle of CH₄ inversions is detailed in the Supplement (Sect. S3), and an overview of the different methods applied to CH₄ is presented in Houweling et al. (2017).

We consider an ensemble of inversions gathering various chemistry-transport models, differing in vertical and horizontal resolutions, meteorological forcing, advection and convection schemes, and boundary layer mixing. Including these different systems is a conservative approach that allows us to cover different potential uncertainties of the inversion: model transport, set-up issues, and prior dependency. General characteristics of the inversion systems are provided in Table 4. Further details can be found in the referenced papers and in the Supplement (Sect. S6). Each group was asked to provide gridded flux estimates for the period 2000–2020, using either surface or satellite data, but no additional constraints were imposed so that each group could use their preferred inversion set-up. Two sets of prior emission distributions were built from the most recent inventories or model-based estimates (see Sect. S4), but its use was not mandatory (see Tables S8 to S11 for the inversion characteristics). This approach corresponds to a flux assessment but not to a model intercomparison as the protocol was not too stringent. Estimating posterior uncertainty is time-consuming and computer-resource-consuming, especially for the 4D-Var approaches and Monte Carlo methods. Posterior uncertainties have not been requested for this study, but they were found to be lower than the ensemble spread in Saunois et al. (2020). Indeed, chemistry-transport models differ in interhemispheric transport, stratospheric CH₄ profiles, and OH distribution, limitations which are not fully considered in the individual posterior uncertainty. As a result, we report the minimum-maximum range among the different top-down approaches.

Seven atmospheric inversion systems using global Eularian transport models were used in this study; they contributed to the previous budgets that included eight atmospheric inversion systems in Saunois et al. (2016) and nine in Saunois et al. (2020). Each inversion system provided one or several simulations, including sensitivity tests varying the assimilated observations (surface or satellite), the OH interannual variability, or the prior flux ensemble. This represents a total of 24 inversion runs with different time coverage: generally, 2000–2020 for surface-based observations
and 2010–2020 for GOSAT-based inversions (Tables 4 and S7). In poorly observed regions, top-down surface inversions may rely on the prior estimates and bring little or no additional information to constrain (often) spatially overlapping emissions (e.g. in India, China). Also, we recall that many top-down systems solve for the total fluxes at the surface only or for some categories that may differ from the GCP categories. When multiple sensitivity tests were performed, the mean of this ensemble was used not to overweight one particular inverse system. It should also be noticed that some satellite-based inversions are in fact combined satellite and surface inversions as they use surface-based inversions to correct the latitudinal bias of the satellite retrievals against the optimised atmosphere measurements to correct for errors in the transport model especially in the stratosphere (e.g. Segers et al., 2022; Maasakkers et al., 2019). Nevertheless, these inversions are still referred to as satellite-based inversions. Most of the top-down models use the OH distribution from the TRANSCOM experiment (Patra et al., 2011) either as fixed over the period or with the interannual variability derived by Patra et al. (2021).

Each group provided gridded monthly maps of emissions for both their prior and posterior total and for sources per category (see the categories Sect. 2.3). Results are reported in Sect. 5. Atmospheric sinks from the top-down approaches have been provided for this budget and are compared with the values reported in Saunois et al. (2020). Not all inverse systems report their chemical sink; as a result, the global mass imbalance for the top-down budget is derived as the difference between total sources and total sinks for each model when both fluxes were reported.

# 5 Methane budget: top-down and bottom-up comparison

## 5.1 Global methane budget

### 5.1.1 Global total methane emissions

At the global scale, the total annual emissions amount inferred by the ensemble of 24 inversions is 575 [553–586] Tg CH₄ yr⁻¹ for the 2010–2019 decade (Table 3), with the highest ensemble mean emission of 608 [581–627] Tg CH₄ yr⁻¹ for 2020. Global emissions for 2000–2009 (543 Tg CH₄ yr⁻¹) are consistent with Saunois et al. (2016, 2020), and the range for global emissions (526–558 Tg CH₄ yr⁻¹) falls within the range in Saunois et al. (2016) (535–569) and Saunois et al. (2020) (524–560), although the ensemble of inverse systems contributing to this budget is different from Saunois et al. (2016, 2020). Changes in ensemble members contributing to the different budgets are a feature of each new GMB release and, therefore, introduce a source of variation (Table S7). The range reported gives the minimum and maximum values among studies and does not reflect the individual full uncertainties. In addition,

most of the top-down models use the same OH distribution from the TRANSCOM experiment (Patra et al., 2011), which introduces less variability to the global budget than is likely justified, so it contributes to the rather low range (10%) compared to bottom-up estimates (see below). We recall here that Zhao et al. (2020a) found an uncertainty of about 17% in global methane emissions (518 to 611 Tg CH₄ yr⁻¹ for the early 2000s) due to changes in OH burden and distribution (OH ranging from 10.3 to 12.6 × 10⁵ molecules cm⁻³)

The bottom-up estimates considered here differ substantially from the top-down results, with annual global emissions being about 15% larger at 669 [512–849] Tg CH₄ yr⁻¹ for 2010–2019 (Table 3). Yet, thanks to the double counting corrections in this budget, bottom-up and top-down budgets are in better agreement compared to previous GMB releases. For the period 2000–2009, the discrepancy between bottom-up and top-down was about 30% of the top-down estimates in Saunois et al. (2016, 2020) (167 and 156 Tg CH₄ yr⁻¹ respectively), a value that has been reduced significantly in this budget (now 95 Tg CH₄ yr⁻¹ (< 17%) for the same 2000–2009 period). This reduction is due to improvements from an important decrease in the estimate of emissions from natural and indirect anthropogenic emissions from bottom-up approaches and more specifically inland freshwater emissions. From the previous budget, the estimate for inland freshwater emissions (lakes, ponds, reservoirs, rivers, and streams) decreased from 159 Tg CH₄ yr⁻¹ to 112 Tg CH₄ yr⁻¹ (47 Tg decrease). Then, 23 Tg was removed in the total freshwater ecosystem emissions due to double counting between vegetated wetlands and mostly small ponds and lakes (Sect. 3.2.2). As a result, the combined wetland and inland freshwater emissions are estimated to be 242 Tg CH₄ yr⁻¹ for 2000–2009 (Table 3), compared with 306 Tg CH₄ yr⁻¹ in Saunois et al. (2020).

This budget is the first that reconciles bottom-up and top-down total emissions within the uncertainty ranges. However, the uncertainty in the global budget remains high because of the large range reported for emissions from freshwater systems. Still, the upper bound of global emissions from bottom-up approaches is not consistent with top-down estimates that rely on OH burden constrained by methyl chloroform atmospheric observations and is still likely overestimated.

### 5.1.2 Global methane emissions per source category

The global CH₄ emissions from natural and anthropogenic sources (see Sect. 2.3) for 2010–2019 are presented in Figs. 6 and 7 and Table 3. Top-down estimates attribute about 65% of total emissions to anthropogenic activities (range of 55%–70%) and 35% to natural emissions. Bottom-up estimates attribute 57% of emissions to direct anthropogenic and the rest to natural plus indirect anthropogenic emissions. A current predominant role of direct anthropogenic sources of CH₄ emissions is consistent with and strongly supported
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Methane global emissions from five broad categories (see Sect. 2.3) for the 2010–2019 decade for top-down inversion models (left light-coloured boxplots) (in Tg CH₄ yr⁻¹) and for bottom-up models and inventories (right dark-coloured boxplots). For combined wetland and inland freshwaters three estimates are given: top-down estimates (left), bottom-up estimates (middle), and bottom-up estimates (right) for wetlands only. Median value and first and third quartiles are presented in the boxes. The whiskers represent the minimum and maximum values when suspected outliers are removed (see Sect. 2.2). Suspected outliers are marked by stars. Bottom-up quartiles are not available for bottom-up estimates, except for wetland emissions. Mean values are represented by “+” symbols; these are the values reported in Table 3.</figcaption></figure>

by available ice core and atmospheric CH₄ records. These data indicate that atmospheric CH₄ varied around 700 ppb during the last millennium before increasing by a factor of 2.6 to ~1800 ppb since pre-industrial times. Accounting for the decrease in mean lifetime over the industrial period, Prather et al. (2012) estimated from these data a total source of 554 ± 56 Tg CH₄ in 2010, of which about 64% (352 ± 45 Tg CH₄) was of direct anthropogenic origin, consistent with the range in our top-down estimates.

## Natural and indirect anthropogenic emissions

Although smaller than in previous Global Methane Budget releases, the main remaining discrepancy between top-down and bottom-up budgets is found for the natural and indirect anthropogenic emission total (105 Tg), with 311 [183–462] Tg CH₄ yr⁻¹ for bottom-up and only 206 [188–225] Tg CH₄ yr⁻¹ for top-down over the 2010–2019 decade (Table 3). In the bottom-up estimates, this discrepancy comes first from the estimates in both inland freshwater sources (64 Tg) and second from other natural sources (20 Tg from geological sources, termites, oceans, and permafrost). The top-down approaches may be biased due to missing fluxes (mainly inland freshwaters) in their prior estimates.

For 2010–2019, the top-down and bottom-up derived estimates for wetlands emissions of 165 [145–214] Tg CH₄ yr⁻¹ and 159 [119–203] Tg CH₄ yr⁻¹ (Table 3) respectively are

comparable within their range. Based on diagnostic wetland area values (see notes in Table 3), bottom-up mean wetland emissions for the 2000–2009 period are smaller in this study than those of Saunois et al. (2016) but larger than in Saunois et al. (2020). The changes in wetland emissions from bottom-up models may be related to updates on the wetland extent data set (WAD2M), the use of two different meteorological forcings for this study, and a different set of models (see Sect. 3.2.1). Conversely, the current 2000–2009 mean top-down wetland estimates are lower than those of Saunois et al. (2016) and Saunois et al. (2020) (Table 3). In the bottom-up estimates, the amplitude of the range of emissions of 116–189 is roughly similar to Saunois et al. (2016) (151–222) and Saunois et al. (2020) (102–179) for 2000–2009. Here, the larger range in bottom-up estimates of wetland emissions is due to the use of GSWP3-W5E5 and greater sensibilities of some models to the climate parameters, as discussed in Sect. 3.2.1. Bottom-up and top-down estimates for wetland emissions agree better in this study (~5 Tg yr⁻¹ for 2000–2009) than in Saunois et al. (2016, 2020) (~17 Tg yr⁻¹ and ~30 Tg yr⁻¹ respectively). Natural emissions from inland freshwater systems were not included in the prior fluxes used in the top-down approaches, due to unavailable or uncertain gridded products at the start of the modelling activity. However, emissions from these inland freshwater systems may be implicitly included in the posterior estimates of the
<figure><img src="image_9.png" /><figcaption>**Figure 7.** The Global Methane Budget for the 2010–2019 decade. Both bottom-up (left) and top-down (right) estimates are provided for each emission and sink category (in Tg CH₄ yr⁻¹), as well as for total emissions and total sinks. Combined wetland and inland freshwaters are depicted as natural and indirect anthropogenic sources (darker green and pink hatches) to recall Fig. 4 (Sect. 3.2.2). Adapted from Saunois et al. (2016, 2020).</figcaption></figure>

top-down models, as these two sources are close and probably overlap at the rather coarse resolution of the top-down models. This is the reason why the “wetland emissions” in the top-down budget in fact better correspond to the sum of combined wetland and inland freshwater emissions in the bottom-up budget. The double counting of 23 Tg CH₄ reduces the bottom-up budget for combined wetland and inland freshwaters from 271 Tg CH₄ yr⁻¹ to 248 Tg CH₄ yr⁻¹ (Sect. 3.2.2). Comparing the 2000–2009 decadal emissions from wetlands and inland freshwater ecosystems estimated by the bottom-up approaches across the last three Global Methane Budget articles shows an upward and then a downward revision with 305 (183 + 122) Tg CH₄ yr⁻¹, 356 (147 + 209) Tg CH₄ yr⁻¹, and 248 (159 + 112 – 23) Tg CH₄ yr⁻¹ (respectively from Saunois et al. (2016, 2020) and this work; the sum in brackets corresponds to the sum of vegetated wetland emissions and inland water emissions estimated through the different budgets). The combined wetland and inland freshwater emissions discrepancy between bottom-up and top-down approaches amounts to 105 Tg CH₄ yr⁻¹ for the 2010–2019 decade. From a top-down point of view, the sum of all the natural sources is more robust than the partitioning between wetlands, inland waters, and other natural sources. Including all known spatiotemporal distributions of natural emissions in top-down prior fluxes would be a step forward in consistently comparing natural versus anthropogenic total emissions between top-down and bottom-up approaches.

In the top-down budget, wetlands represent 28% on average of the total methane emissions but only 24% in the bottom-up budget (because of higher total emissions inferred) (see Table 3). Given the large uncertainties, neither bottom-up nor top-down approaches included in this study point to significant changes in wetland emissions between the two decades 2000–2009 and 2010–2019 at the global scale.

For the 2010–2019 decade, top-down inversions infer “other natural emissions” (Table 3) at 43 [40–46] Tg CH₄ yr⁻¹, whereas the sum of the individual bottom-up emissions is 63 [24–93] Tg CH₄ yr⁻¹, contributing to a 20 Tg discrepancy between bottom-up and top-down approaches. Atmospheric inversions infer the same amount over the decade 2000–2009 as over 2010–2019, which is almost half of the value reported in Saunois et al. (2016) (68 [21–130] Tg CH₄ yr⁻¹). This reduction in magnitude and uncertainty is due to (1) a more consistent way of considering other natural emissions in the various inverse systems (same prior estimate as in this budget) and (2) a difference in the ensemble of top-down inversions reported here compared to previous releases. It is worth noting that most of the top-down models include about the same ocean and onshore geological emissions and termite emissions in their prior scenarios. However, none include freshwater or permafrost emissions in their prior fluxes and thus in their posterior estimates.

Geological emissions are associated with relatively large uncertainties, and marine seepage emissions are still widely
debated (Thornton et al., 2020). However, summing up all bottom-up fossil-CH₄-related sources (including anthropogenic emissions) leads to a total of 165 [135–190] Tg CH₄ yr⁻¹ in 2010–2019, which is about 29% of the top-down global CH₄ emissions and 25% of the bottom-up total global estimate. These results agree with the value inferred from ¹⁴C atmospheric isotopic analyses of 30% contribution of fossil-CH₄ to global emissions (Etiope et al., 2008; Lassey et al., 2007b). These total fossil fuel emissions from bottom-up approaches agree well with the ¹³C-based estimate of Schwietzke et al. (2016) of 192 ± 32 Tg CH₄ yr⁻¹. In the bottom-up budget, the larger total emissions (due to uncertainties in bottom-up estimates of natural emissions) lead to a lower fossil fuel contribution compared to Lassey et al. (2007b).

### Direct anthropogenic emissions

Total direct anthropogenic emissions for the period 2010–2019 were assessed to be statistically consistent between top-down (369 Tg CH₄ yr⁻¹, range 350–391) and bottom-up approaches (358 Tg CH₄ yr⁻¹, range 329–387); however, top-down approaches infer direct anthropogenic emissions larger by 11 Tg CH₄ yr⁻¹ on average compared to bottom-up approaches (Table 3). The partitioning of direct anthropogenic emissions between agriculture and waste, fossil fuels extraction and use, and biomass and biofuel burning also shows good consistency between top-down and bottom-up approaches, though top-down approaches still suggest less fossil fuel and more agriculture and waste emissions than bottom-up estimates (Table 3 and Figs. 6 and 7). For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH₄ yr⁻¹ in the top-down budget and 211 [195–231] Tg CH₄ yr⁻¹ in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH₄ yr⁻¹ in the top-down budget and 120 [117–125] Tg CH₄ yr⁻¹ in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH₄ yr⁻¹ in the top-down budget and 28 [21–39] Tg CH₄ yr⁻¹ in the bottom-up budget. Biofuel CH₄ emissions rely on very few estimates currently (Wuebbles and Hayhoe, 2002). Although biofuel is a small source globally (~12 Tg CH₄ yr⁻¹), more estimates are needed to allow a proper uncertainty assessment. Overall for top-down inversions the global fraction of total emissions for the different source categories is 40% for agriculture and waste, 20% for fossil fuels, and 5% for biomass and biofuel burning. With the exception of biofuel emissions, the uncertainty associated with global anthropogenic emissions appears to be smaller than that of natural sources but with an asymmetric uncertainty distribution (mean significantly different than median). The relative agreement between top-down and bottom-up approaches may indicate a limited capability of the inversion to separate emissions and a dependency to their prior fluxes; this agreement should therefore be treated with caution. Indeed, in poorly observed regions, top-down inversions rely

on the prior estimates and bring little or no additional information to constrain (often) spatially overlapping emissions (e.g. in India, China). Also, as many top-down systems solve for the total fluxes at the surface or for some categories that may differ from the GCP categories, their posterior partitioning relies on the prior ratio between categories that are prescribed using bottom-up inventories.

### 5.1.3 Global budget of total methane sinks

#### Top-down estimates

The annual CH₄ chemical removal from the atmosphere is estimated to be 521 Tg CH₄ yr⁻¹ averaged over the period 2010–2019, with an uncertainty of about ±2% (range 485–532 Tg CH₄ yr⁻¹) (Table 3). All the inverse models account for CH₄ oxidation by OH and O(¹D), and some include stratospheric Cl oxidation (Table S8 to S11). Most of the top-down models use the OH distribution from the TRANSCOM experiment (Patra et al., 2011) either as fixed over the period or including interannual variability from Patra et al. (2021). This study shows no trend in OH and IAV below ±4%, in agreement with Thompson et al. (2024) (no significant OH trend and IAV < 2%). As a result, the range of the top-down sink estimates is rather low compared to bottom-up estimates (see below). Differences between transport models affect the chemical removal of CH₄, leading to different chemical loss rates, even with the same OH distribution. However, uncertainties in the OH distribution and magnitude (around ±10% at the global scale; Zhao et al., 2019) are not considered in our study, while they could contribute to a significant change in the chemical sink and then in the derived posterior emissions through the inverse process (Zhao et al., 2020a), around ±17% at the global scale, much larger than the model spread derived here. The chemical sink represents more than 90% of the total sink, the rest being attributable to soil uptake (35 [35–36] Tg CH₄ yr⁻¹). The rather narrow range is due to the use of the same climatological soil sink provided within the modelling protocol, which is based on Murguia-Flores et al. (2018). This sink estimate used as a prior estimate in the inversions is a bit higher than the mean estimate of the soil sink calculated by bottom-up models (30 Tg CH₄ yr⁻¹; Sect. 3.3.4).

#### Bottom-up estimates

The total chemical loss for the 2010s reported here is 602 Tg CH₄ yr⁻¹ with an uncertainty of 21% (~125 Tg CH₄ yr⁻¹). Differences in chemistry schemes in the models (especially in the stratosphere) and in the volatile organic compound treatment probably explain most of the discrepancies among models (Zhao et al., 2019).
## 5.2 Latitudinal and regional methane budgets

The latitudinal and regional breakdown of the bottom-up budget is based on crude assumptions that we acknowledge here. Natural and indirect anthropogenic emissions are based on wetland gridded products from land surface models and the combination of the maps from lakes and ponds from Johnson et al. (2022b), reservoirs from Johnson (2021), and streams and rivers from Rocher-Ros (2023) and Rocher-Ros (2023), the sum of those three scaled to 89 Tg CH₄ yr⁻¹ (shown in Fig. 5) to artificially include the double counting (estimated only at the global scale) and match the global estimate. However, we acknowledge that this procedure distributes the double counting relatively to the final emission distribution and not according to the freshwater ecosystems where the double counting probably occurs. Wild animal and permafrost maps do not exist and are missing from the calculation, leading to at least 3 Tg CH₄ yr⁻¹ of discrepancy. However, as mentioned previously (Sect. 3.2.5 and 3.2.7), this 3 Tg CH₄ yr⁻¹ estimate is probably underestimated in the bottom-up budget. Geological and ocean sources are based on Etiope et al. (2019) and Weber et al. (2019) gridded products scaled to 50 Tg CH₄ yr⁻¹ to be consistent with the reported global values. Finally, we use the termite emission map produced for this budget and used in the global budget. The latitudinal budget does not include the estimates from the FAO and the USEPA for the direct anthropogenic emissions as they are only provided at country scale.

### 5.2.1 Latitudinal budget of total methane emissions

The latitudinal breakdown of emissions inferred from atmospheric inversions reveals a dominance of emissions in the latitudinal band 90° S–30° N of 364 [337–390] Tg CH₄ yr⁻¹, representing 64% of the global total (Tables 5 and 6). As emissions in the tropics (30° S–30° N) dominate this latitudinal contribution, we may refer to 90° S–30° N as the tropics in the following 32% of the emissions are from the mid-latitudes (187 [160–204] Tg CH₄ yr⁻¹) and 4% from high latitudes (above 60° N). The amounts of emissions depend on the land surface area of the region; however, the 90° S–30° N latitudinal band represents 53% of global land surfaces and the boreal region 60–90° N around 13%. Hence, the relative contribution of the emissions from the 90° S–30° N region is much larger (11 percentage points more) than the percentage of its land surface areas; on the contrary the boreal regions (60–90° N) emissions contribute significantly less than the surface area percentage of this region (9 percentage points less). The ranges around the mean latitudinal emissions are larger than for the global CH₄ sources. While the top-down uncertainty is less than ±5% at the global scale, it increases to ±7% for the tropics, to ±12% the northern mid-latitudes, and to more than ±20% in the northern high latitudes (for 2010–2019; Table 5). Both top-down and bottom-up approaches consistently show that CH₄

decadal emissions have increased by +21–27 Tg CH₄ yr⁻¹ in the tropics and by +5–16 Tg CH₄ yr⁻¹ in the northern mid-latitudes between 2000–2009 and 2010–2019 using the mean ensemble estimate.

Over 2010–2019, at the global scale, satellite-based inversions infer almost identical emissions to ground-based inversions (difference of +1 [−3 to 9] Tg CH₄ yr⁻¹, with GOSAT-based inversion a bit higher than surface measurements-based inversions), when comparing consistently surface versus satellite-based inversions for each system, similar to Saunois et al. (2020). This difference is much lower than the range derived between the different systems (range of 20 Tg CH₄ yr⁻¹ using surface- or satellite-based inversions). This result reflects that differences in atmospheric transport among the systems probably have more impact on the estimated global emissions than the types of observations assimilated.

As expected, considering the different coverage of observation data sets, regional distributions of inferred emissions differ depending on the nature of the observations used (satellite or surface). The largest differences (satellite-based minus surface-based inversions) are observed over the tropical region, between −10 and +43 Tg CH₄ yr⁻¹ (90° S to 30° N) and the northern mid-latitudes (between −36 and −2 Tg CH₄ yr⁻¹). Satellite data provide stronger constraints on fluxes in tropical regions than surface data, due to a much larger spatial coverage. It is therefore not surprising that differences between these two types of observations are found in the tropical band and consequently in the northern mid-latitudes to balance total emissions, thus affecting the north-south gradient of emissions. However, the regional patterns of these differences are not consistent through the different inverse systems. Indeed, some systems found higher emissions in the tropics when using GOSAT instead of surface observations, while others found the opposite. This difference between inversion systems may depend on whether or not a bias correction is applied to the satellite data based on surface observations and also on the modelled horizontal and vertical transports, in the troposphere and in the stratosphere.

### 5.2.2 Latitudinal methane emissions per source category

The analysis of the latitudinal CH₄ budget per source category (Fig. 8 and Table 6) can be performed both for bottom-up and top-down approaches but with limitations. Bottom-up estimates of natural and indirect anthropogenic emissions are based on assumptions as specified at the beginning of this Sect. 5.2. For top-down estimates, as already noted, the partitioning of emissions per source category has to be considered with caution. Indeed, using only atmospheric CH₄ observations to constrain CH₄ emissions makes this partitioning largely dependent on prior emissions. However, differences in spatial patterns and seasonality of emissions can be utilised to constrain emissions from different categories
by atmospheric methane observations (for those inversions solving for different sources categories, see Sect. 2.3).

Agriculture and waste are the largest sources of CH₄ emissions in the tropics and the Southern Hemisphere (140 [121–150] Tg CH₄ yr⁻¹ in the bottom-up budget and 150 [135–168] Tg CH₄ yr⁻¹ in the top-down budget, about 40 % of total CH₄ emissions in this region) (Table 6). However, combined wetland and inland freshwater emissions are nearly as large with 151 [85–234] Tg CH₄ yr⁻¹ in the bottom-up budget and 128 [112–155] Tg CH₄ yr⁻¹ in the top-down budget (Table 6). Anthropogenic emissions dominate in the northern mid-latitudes, with the highest contribution from agriculture and waste emissions (40 % of total emissions in the top-down budget), closely followed by fossil fuel emissions (32 % of total emissions, top-down budget). Boreal regions are largely dominated by inland freshwater emissions (41 % and 54 % of total emissions, top-down and bottom-up budget respectively) (Table 6).

The largest discrepancies between the top-down and the bottom-up budgets are found in the mid-latitudes and boreal regions from the natural and indirect sources with bottom-up estimates twice as large as the top-down ones, especially in the inland freshwater category.

The uncertainty for wetlands and inland freshwater emissions is larger in the bottom-up models than in the top-down models (mostly wetlands), while uncertainty in anthropogenic emissions is larger in the top-down models than in the bottom-up inventories. The large uncertainty in tropical inland freshwater emissions (mostly wetlands) of ±44 % results from large regional differences between the bottom-up land surface models. Although they are using the same forcings, their responses in terms of flux density show different sensitivities to temperature, water vapour pressure, precipitation, and radiation.

### 5.2.3 Regional budget for total emissions

The regional breakdown of emissions is provided for 18 continental regions (see map in Fig. S3 and Table S1 with the country aggregation in the Supplement).

At the regional scale and for the 2010–2019 decade (Table 7), total methane emissions are dominated by Southeast Asia with 63 [52–71] Tg CH₄ yr⁻¹, China with 57 [37–72] Tg CH₄ yr⁻¹, and South Asia with 52 [43–60] Tg CH₄ yr⁻¹ (top-down budget). These top three emitters contribute 30 % of total global CH₄ emissions. The following high-emitting regions are Brazil with 47 [41–58] Tg CH₄ yr⁻¹, equatorial Africa with 47 [39–59] Tg CH₄ yr⁻¹, the USA with 38 [32–46] Tg CH₄ yr⁻¹, southwestern South America with 38 [30–48] Tg CH₄ yr⁻¹, Russia with 36 [27–45] Tg CH₄ yr⁻¹, Europe with 31 [24–36] Tg CH₄ yr⁻¹, the Middle East with 31 [24–39] Tg CH₄ yr⁻¹, northern Africa with 25 [23–29] Tg CH₄ yr⁻¹, and Canada with 20 [17–24] Tg CH₄ yr⁻¹. Other regions contribute less than 20 Tg CH₄ yr⁻¹.

### 5.2.4 Regional budget per source category

In agreement with Stavert et al. (2021), natural and indirect anthropogenic emissions are dominated by Brazil, Canada, Russia, equatorial Africa, and Southeast Asia, contributing 126 Tg CH₄ yr⁻¹ in the bottom-up and 105 Tg CH₄ yr⁻¹ in the top-down budget (Table 7), i.e. 47 % and 50 % of the global natural and indirect anthropogenic emissions in these budgets respectively. At regional scale also, the ranges of uncertainty in natural and indirect anthropogenic emissions are much larger in the bottom-up budget than in the top-down budget (Fig. S5). Except for four regions (Canada, Brazil, northern South America, southwestern South America), direct anthropogenic emissions contribute more than half of the total regional emissions. Due to the large uncertainty and discrepancies in natural and indirect emissions estimates, the regional direct anthropogenic fractions may differ between the bottom-up and top-down budgets. However, in absolute values, the highest direct anthropogenic emitters are the same in the two budgets, with China and South Asia being the top two by far, contributing 56 [51–66] Tg CH₄ yr⁻¹ and 45 [44–47] Tg CH₄ yr⁻¹, respectively (bottom-up values; Fig. 9 and Table 7). These two regions contribute 28 % (26 %) of the global direct anthropogenic emissions in the bottom-up (top-down) budget. The ranks of direct anthropogenic emitters are similar to those presented in the last budget (Stavert et al., 2021). Southeast Asia, the USA, the Middle East, Europe, equatorial Africa, and Russia emit between 32 and 23 Tg CH₄ yr⁻¹ as direct anthropogenic emissions (bottom-up values; Fig. 8). Brazil, northern Africa, and southwestern South America emit between 10 and 20 CH₄ yr⁻¹, while the rest of the regions emit less than 10 CH₄ yr⁻¹ direct anthropogenic emissions (Table 7 and Fig. S5).

The sectoral partitioning at the regional scale has been derived from both bottom-up and top-down approaches. However, the top-down budget has more limitations, as the sectoral partitioning is usually based on the prior fluxes fractions at the pixel scale, and assimilating only total methane observations does not allow one to disentangle the different source sectors overlapping in a pixel grid. However, differences in spatial patterns and seasonality of emissions can still be constrained by atmospheric CH₄ observations for those inversions solving for different sources categories (see Sect. 2.3).

Bottom-up approaches allow deeper sectorial splitting, especially in terms of direct anthropogenic emissions (Fig. 9). Table 7 and Figs. 9 and 10 present the estimations of CH₄ emissions on average over 2010–2019. Figure 10 presents the budgets for three main categories (combined wetland and inland freshwaters, fossil fuels, and agriculture and waste); a more detailed figure and table including the five categories is available in the Supplement (Fig. S6 and Tables S13 to S18). Values for each individual data set for the decades 2000–2009, 2010–2019, and the last year 2020 are made available in a spreadsheet (see “Data availability”).
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Methane latitudinal emissions from five broad categories (see Sect. 2.3) for the 2010–2019 decade for top-down inversion models (left light-coloured boxplots) (in Tg CH₄ yr⁻¹) and for bottom-up models and inventories (right dark-coloured boxplots). For combined wetland and inland freshwaters three estimates are given: top-down estimates (left), bottom-up estimates (middle), and bottom-up estimates (right) for wetlands only. Median value and the first and third quartiles are presented in the boxes. The whiskers represent the minimum and maximum values when suspected outliers are removed (see Sect. 2.2). Suspected outliers are marked by stars. Bottom-up quartiles are not available for bottom-up estimates, except wetland emissions. Mean values are represented by “+” symbols; these are the values reported in Table 6.</figcaption></figure>

<table><caption>**Table 7.** Regional methane emissions (regions ranked by continent) (in Tg CH₄ yr⁻¹) for the last decade of 2010–2019, based on top-down and bottom-up approaches. Uncertainties are reported as [min–max] range of reported studies. Differences of 1 Tg CH₄ yr⁻¹ in the totals can occur due to rounding errors. For bottom-up approaches, natural and indirect anthropogenic sources are estimated based on available gridded data sets (see text in Sect. 5.2). As some emissions are missing gridded products (wild animals, permafrost, and hydrates), discrepancies may occur in terms of totals proposed in Table 3. Bottom-up direct anthropogenic estimates are based on all products (gridded and per country).</caption><thead><tr><th rowspan="2">Region</th><th colspan="2">Total emissions</th><th colspan="2">Natural and indirect anthropogenic emissions</th><th colspan="2">Direct anthropogenic emissions</th></tr><tr><th>Bottom-up</th><th>Top-down</th><th>Bottom-up</th><th>Top-down</th><th>Bottom-up</th><th>Top-down</th></tr></thead><tbody><tr><td>USA</td><td>49 [27–77]</td><td>38 [32–46]</td><td>24 [7–43]</td><td>12 [7–22]</td><td>26 [19–34]</td><td>25 [16–31]</td></tr><tr><td>Canada</td><td>38 [14–71]</td><td>20 [17–24]</td><td>32 [11–63]</td><td>14 [11–22]</td><td>6 [3–8]</td><td>7 [5–9]</td></tr><tr><td>Central America</td><td>18 [10–28]</td><td>17 [14–19]</td><td>8 [3–17]</td><td>5 [2–6]</td><td>10 [8–12]</td><td>12 [11–13]</td></tr><tr><td>Northern South America</td><td>19 [9–35]</td><td>16 [13–20]</td><td>10 [3–17]</td><td>9 [7–11]</td><td>9 [6–17]</td><td>7 [6–8]</td></tr><tr><td>Brazil</td><td>51 [26–79]</td><td>47 [41–58]</td><td>32 [11–57]</td><td>26 [22–36]</td><td>19 [16–22]</td><td>21 [17–26]</td></tr><tr><td>Southwestern South America</td><td>34 [16–51]</td><td>38 [30–48]</td><td>21 [6–35]</td><td>24 [16–34]</td><td>13 [10–16]</td><td>14 [12–17]</td></tr><tr><td>Europe</td><td>42 [29–57]</td><td>31 [24–36]</td><td>17 [6–30]</td><td>7 [5–9]</td><td>25 [22–27]</td><td>24 [20–31]</td></tr><tr><td>Northern Africa</td><td>24 [18–33]</td><td>25 [23–29]</td><td>7 [2–13]</td><td>6 [6–8]</td><td>18 [16–20]</td><td>19 [17–21]</td></tr><tr><td>Equatorial Africa</td><td>47 [28–83]</td><td>47 [39–59]</td><td>23 [10–49]</td><td>24 [20–30]</td><td>24 [19–34]</td><td>23 [19–29]</td></tr><tr><td>Southern Africa</td><td>21 [5–43]</td><td>19 [16–24]</td><td>11 [2–29]</td><td>8 [7–10]</td><td>10 [3–14]</td><td>11 [10–12]</td></tr><tr><td>Russia</td><td>48 [24–83]</td><td>36 [27–45]</td><td>25 [9–47]</td><td>14 [11–18]</td><td>23 [15–36]</td><td>21 [14–29]</td></tr><tr><td>Central Asia</td><td>15 [6–29]</td><td>10 [8–13]</td><td>8 [2–19]</td><td>1 [0–2]</td><td>8 [4–10]</td><td>9 [7–11]</td></tr><tr><td>Middle East</td><td>35 [21–47]</td><td>31 [24–39]</td><td>9 [3–15]</td><td>4 [1–6]</td><td>26 [18–31]</td><td>28 [20–34]</td></tr><tr><td>China</td><td>71 [55–99]</td><td>57 [37–72]</td><td>15 [4–33]</td><td>4 [3–7]</td><td>57 [51–66]</td><td>53 [34–66]</td></tr><tr><td>Korea–Japan*</td><td>6 [4–12]</td><td>5 [4–6]</td><td>3 [1–7]</td><td>1 [1–1]</td><td>4 [3–5]</td><td>4 [3–5]</td></tr><tr><td>South Asia</td><td>58 [49–72]</td><td>52 [43–60]</td><td>13 [5–25]</td><td>6 [5–6]</td><td>45 [44–47]</td><td>45 [37–49]</td></tr><tr><td>Southeast Asia</td><td>64 [42–93]</td><td>63 [52–71]</td><td>32 [19–54]</td><td>27 [20–34]</td><td>32 [23–39]</td><td>35 [31–46]</td></tr><tr><td>Australasia</td><td>16 [9–26]</td><td>13 [10–17]</td><td>10 [4–19]</td><td>6 [4–7]</td><td>7 [6–7]</td><td>7 [6–7]</td></tr></tbody></table>

<footnote>* Korea collectively refers North Korea and South Korea in this table.</footnote>
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Regional anthropogenic emissions for the 2010–2019 decade from bottom-up estimates (in Tg CH₄ yr⁻¹). Regions are ranked by their total anthropogenic emissions. Note that each category has its own emission scale. Note that Korea collectively refers to North Korea and South Korea in this figure.</figcaption></figure>

For most regions, “combined wetland and inland freshwater emissions” are the most uncertain in the bottom-up budget, and generally their range is larger than in the top-down budget. In the top-down budget for 2010–2019 (Table 7), this category contributes the most to the regional emissions in Brazil with 24 [20–33] Tg CH₄ yr⁻¹, Southeast Asia with 24 [14–29] Tg CH₄ yr⁻¹ (though similar to their agriculture and waste emissions of 24 [21–31] Tg CH₄ yr⁻¹), equatorial Africa with 22 [19–28] Tg CH₄ yr⁻¹, southwestern South America with 22 [14–33] Tg CH₄ yr⁻¹, Canada with 12 [9–18] Tg CH₄ yr⁻¹, northern South America with 8 [6–10] Tg CH₄ yr⁻¹, southern Africa with 7 [4–9] Tg CH₄ yr⁻¹. The agriculture and waste emissions category dominates in South Asia with 39 [33–43] Tg CH₄ yr⁻¹, China with 30 [13–37] Tg CH₄ yr⁻¹, Europe with 19 [16–23] Tg CH₄ yr⁻¹, the USA with 13 [9–16] Tg CH₄ yr⁻¹, northern Africa with 13 [12–14] Tg CH₄ yr⁻¹, Central America with 9 [8–10] Tg CH₄ yr⁻¹, and North and South Korea and Japan with 3 [3–4] Tg CH₄ yr⁻¹. Fossil fuel emissions dominate in the Middle East with 18 [11–24] Tg CH₄ yr⁻¹ and Russia with 14 [8–23] Tg CH₄ yr⁻¹ (close to their combined wetland and inland freshwater emissions of 11 [8–13] Tg CH₄ yr⁻¹).

The four largest contributors to the fossil fuel sector remain China, the Middle East, Russia, and the USA. Altogether they contribute 67 (64) Tg CH₄ yr⁻¹ in the bottom-up (top-down) budget, around 55% of the global fossil fuel

emissions. The bottom-up and top-down approaches generally agree in terms of ensemble mean, except for China for which the top-down estimates suggest lower emissions than the inventories. While Chinese fossil fuel emissions occur mainly through coal mining activity (88%), the Middle East, Russia, and the USA extract mainly oil and gas (100%, 80%, 72%).

The three largest contributors to the agriculture and waste sector remain South Asia, China, and Southeast Asia. Together they contribute 88 (92) Tg CH₄ yr⁻¹ in the bottom-up (top-down) budget, around 40% of the global agriculture and waste sector (Table 7). While the ensemble means tend to agree between bottom-up and top-down budgets, the uncertainty derived from the top-down approaches is larger, especially for these three regions. CH₄ emissions due to rice cultivation originate mostly from these same three regions (Southeast Asia, China, and South Asia). Livestock management emissions occur mainly in South Asia with 20 [18–22] Tg CH₄ yr⁻¹, Brazil with 12 [11–13] Tg CH₄ yr⁻¹, China with 11 [8–16] Tg CH₄ yr⁻¹, and Europe with 11 [10–12] Tg CH₄ yr⁻¹ (bottom-up estimates; Table 7). The USA, equatorial Africa, northern Africa, and southwestern South America emit between 7 Tg CH₄ yr⁻¹ and 10 Tg CH₄ yr⁻¹ in this subsector. Other regions emit less than 4 Tg CH₄ yr⁻¹ in the livestock management sector. The waste sector emissions are dominated by three regions: China with 11 [6–14] Tg
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Regional emissions (in Tg CH₄ yr⁻¹) for three broad main emissions categories for the 2010–2019 decade: combined wetland and inland freshwaters, fossil fuel, and agriculture and waste from top-down estimates (left boxplot) and bottom-up estimates (right boxplots). The inner map shows the region’s distribution (also see the Supplement, Table S1 and Fig. S3). More categories are presented in the Supplement in Fig. S6. Note that Korea collectively refers to North Korea and South Korea in this figure.</figcaption></figure>

CH₄ yr⁻¹, South Asia with 9 [4–11] Tg CH₄ yr⁻¹, and Europe with 8 [6–12] Tg CH₄ yr⁻¹ (bottom-up estimates; Table 7). These three regions contribute around 40% of the global emissions of the waste sector. It is worth noting that the uncertainty in the inventory estimates at the regional scale is around 40% (from the min–max range of the estimate, not including the uncertainty from each inventory).

## 6 Insights on the methane cycle from 2020–2022, during which there were unprecedented high growth rates of methane emissions

The mean emissions estimate for the last year of the budget (2020) was 608 [581–627] Tg CH₄ yr⁻¹ (top-down), with 65% of the emissions from direct anthropogenic sources. This is 65 Tg CH₄ yr⁻¹ higher (11%) than the mean emissions of the 2000–2009 decade and 6% higher than 2010–2019. In Jackson et al. (2024), we estimated that total methane emissions increased by around 20% between the early 2000s (2000–2002) and the late 2010s (2018–2020).

Year 2020 was the second highest year in terms of atmospheric CH₄ growth rate (+15.2 ppb yr⁻¹) since systematic measurements began in the late 1980s, coming in just behind the highest in 2021 at 17.97 ppb yr⁻¹. A few studies analysed the large growth rate increase between 2019 (+9.7 ppb yr⁻¹) and 2020 (+15.2 ppb yr⁻¹) of +5.4 ppb yr⁻¹ (corresponding to +14.4 ± 2.0 Tg CH₄ yr⁻¹) (Peng et al., 2022; Stevenson et al., 2022). Peng et al. (2022) estimated that the 2019–2020 growth rate change was almost equally due to an increase in wetland emissions (6.9 ± 2.1 Tg CH₄ yr⁻¹) and a decrease in the OH chemical loss (7.5 ± 0.8 Tg CH₄ yr⁻¹) due to reduced OH precursor emissions during the COVID lockdown (Laughner et al., 2021). The COVID-19 lockdown resulted in decreased NOₓ emissions and reduced fossil-fuel-related CH₄ emissions (Thorpe et al., 2023), leading to less OH production. At the global scale, Feng et al. (2023) calculated an emission increase of 27 Tg CH₄ yr⁻¹ between 2019 and 2020 considering constant OH and a smaller increase of 21 Tg CH₄ yr⁻¹ when including a 1.4% decrease in OH. Increased emissions were mainly found in the northern tropics.
Qu et al. (2022) also inferred a 31 Tg CH₄ yr⁻¹ increase in emissions, mostly in the tropics, half of it in Africa. Furthermore, Niwa et al. (2024) suggested emission increases by 10–18 Tg CH₄ yr⁻¹ in 15° S–10° N and by 20 Tg CH₄ yr⁻¹ in 10–35° N from 2016–2019 to 2020–2022. Such a result is compatible with wetland driven abnormal emissions during a consecutive 3-year La Niña event spanning from 2020 to 2022 (Zhang et al., 2023; Nisbet et al., 2023). The difference in terms of methodology and approaches between these three studies makes it difficult to compare them quantitatively but provides a robust understanding on the possible causes. Importantly, all the studies indicate, in various proportions, increasing CH₄ emissions in the tropics and in the boreal region, potentially driven by microbial emission from wetlands due to wetter and warmer climate, and a significant contribution of reduced OH concentrations due to the COVID lockdown.

Based on our ensemble of data, we find that top-down approaches infer a much larger change in CH₄ emissions (median [Q1–Q3] at +23 [10–31] Tg CH₄ yr⁻¹) than bottom-up approaches (−1 [−5 to 3] Tg CH₄ yr⁻¹) between 2019 and 2020 (Fig. S7). Bottom-up approaches suggest a very small increase in wetland emissions (around +1 [0–3] Tg CH₄ yr⁻¹), while top-down approaches suggest on average a larger increase for wetlands of +8 [5–11] Tg CH₄ yr⁻¹, mainly in the tropics and mid-latitudes. It is worth noting that large uncertainties exist for a given year and that the interannual variability is much lower than the ensemble spread. While bottom-up approaches suggest almost constant fossil fuel emissions and slight increase in agriculture and waste (+3 Tg CH₄ yr⁻¹), top-down approaches tend to derive higher emissions changes (+6 Tg CH₄ yr⁻¹ from the fossil fuel sector and +11 Tg CH₄ yr⁻¹ from agriculture and waste as the median over the ensemble). Biomass burning emissions decreased using both approaches by about 5 Tg CH₄ yr⁻¹, in agreement with Peng et al. (2022). Some inversions were run with IAV of OH from Patra et al. (2021) and others with constant OH. However, the inferred OH IAVs in 2019 and 2020 are rather low (0.3 % and 0.15 % on yearly average) in Patra et al. (2021), leading to a small impact in terms of emissions changes between 2019–2020, with +22 [9–31] (median [Q1–Q3]) based on the inversions with constant OH and 19 [7–28] based on the inversions with varying OH (Fig. S8).

This first analysis based on our ensemble shows how challenging it is to attribute CH₄ emissions changes to a specific sector or region between two years, because related uncertainties remain much larger than the targeted signal to explain. This calls again for further improvement of both approaches.

NOAA estimates 2021 and 2022 methane atmospheric growth rates of 17.8.0 ± 0.5 and 14.0 ± 0.8 ppb yr⁻¹ respectively (Lan et al., 2024). They show a continuation of very high growth rates, challenging again our understanding of the methane budget. The very high values of CH₄ growth

rate over 2020–2022 have also been accompanied by a sharp decline in the stable isotopic signal, δ¹³CCH₄, which suggests that this recent increase in methane growth rate is at least partly explained by increased emissions from microbial sources such as those found in wetlands, inland waters, agriculture, and waste systems (Nisbet et al., 2023; Michel et al., 2024). However, it is worth noting that almost all aforementioned published top-down studies include constraints only on CH₄ and do not discuss the consistency with the atmospheric isotopic signal.

At the time of submission of the manuscript, bottom-up estimates for anthropogenic emissions for 2021 and 2022 were only available from the EDGARv8 data set (https://edgar.jrc.ec.europa.eu/dataset_ghg80, last access: 1 April 2025; EDGAR, 2023). This research inventory suggests that anthropogenic emissions continued to increase from 2020 (374 Tg CH₄ yr⁻¹) to 2021 (379 Tg CH₄ yr⁻¹) and 2022 (386 Tg CH₄ yr⁻¹) with around 62 % of the increase due to the fossil fuel sources, 23 % from the waste sector, and 14 % from the agriculture sector (Table S19). The bottom-up estimate of wetland emissions for 2021–2023, derived from a single wetland model, indicates positive anomalies of 26 Tg CH₄ yr⁻¹ in 2020, 23 Tg CH₄ yr⁻¹ in 2021, and 21 Tg CH₄ yr⁻¹ 2022 relative to the 2000–2006 baseline (https://earth.gov/ghgcenter/data-catalog/ lpjeosim-wetlandch4-grid-v1, last access: 1 April 2025; Zhang et al., 2023).

## 7 Future developments, missing elements, and remaining uncertainties

In this budget, robust features and uncertainties on sources and sinks estimated by bottom-up or top-down approaches have been highlighted as well as discrepancies between the two budgets. Limitations of the different approaches have also been highlighted. Four shortcomings of the CH₄ budget were already identified in Kirschke et al. (2013) and Saunois et al. (2016, 2020) and are revisited below, pointing to key research areas. Although much progress has been made, they are still relevant, and actions are needed. However, these actions fall into different timescales and actors. Here, we revisit the four shortcomings of the contemporary methane budget and discuss how each weakness has been addressed since Saunois et al. (2020). Each section ends by discussing remaining research needs with a list of suggestions, from higher to lower priority.

1. *Shortcoming 1.* The amount of methane emitted by wetland and inland water systems are still highly uncertain, and double counting may remain.

This first shortcoming has probably received the largest interest in the last few years with significant improvements. First, a community effort has been made based on more studies, documenting or modelling more inland freshwater
systems and synthesising emissions from the complex and heterogeneous ensemble of emitting areas: wetlands, ponds, lakes, reservoirs, streams, rivers, estuaries, and marine systems. The range of wetland and inland water emissions has been narrowed down with improved wetland extent and refined estimates for inland freshwater systems. Double counting between inland freshwater systems has been estimated for the first time and accounted for in this budget. All these improvements decreased the discrepancy between top-down and bottom-up estimates of combined wetland and inland freshwater emissions from $156 \text{Tg CH}_4 \text{yr}^{-1}$ in Saunois et al. (2020) down to $85 \text{Tg CH}_4 \text{yr}^{-1}$ in this update for the 2000–2009 decade. Gridded maps for lakes, ponds, reservoirs, and streams and rivers freshwater emissions have been produced over the past years (Johnson et al., 2021, 2022a; Johnson, 2021; Rocher-Ros et al., 2023; Rocher-Ros, 2023), making the spatial distribution of CH$_4$ sources almost complete for the first time and allowing a better description of prior emissions in future top-down inversions.

Next steps in the short term are listed below from the highest to lowest priority:

i. Integration of spatial distribution of inland waters in atmospheric inversion models is needed to reach a full description of prior methane sources and sinks.

ii. Refinement is needed of double counting estimation and its possible reduction with more precise spatial and temporal distributions of the different systems contributing to inland freshwater emissions by using very high-resolution satellite data (down to metre resolutions) to properly separate them. The development is needed of a dynamical global high-resolution (typically few metres) classification of saturated soils and inundated surfaces based on satellite data (visible and microwave), surface inventories, and expert knowledge.

iii. Ongoing efforts need to be continued to calibrate and evaluate land surface models for wetland emissions against in situ observations such as FLUXNET-CH$_4$ (Knox et al., 2019; Delwiche et al., 2021) or BAWLD-CH$_4$ (Kuhn et al., 2021) for boreal regions and to avoid dependence on top-down estimates. It is still critical to increase the limited number of tropical observations and to assimilate them in the inverse systems to help address the issue (e.g. Kallingal et al., 2024).

iv. Ongoing efforts need to be continued to develop a diversity of modelling approaches (among them process-based model or machine learning approaches) to estimate wetland and inland freshwater CH$_4$ emissions, including lateral fluxes and reducing upscaling issues, as done by e.g. Zhuang et al. (2023) for lakes.

v. There needs to be continuous integration of collected flux measurements such as in the FLUXNET-CH$_4$ activity (Knox et al., 2019; Delwiche et al., 2021) or in

the BAWLD-CH$_4$ data set (Kuhn et al., 2021) to provide global flux maps based on machine learning approaches or other approaches (Peltola et al., 2019; McNicol et al., 2023).

In the long run, developing measurement systems will help to improve estimates of the diversity of wetland and inland freshwater sources and further reduce uncertainties:

- More systematic measurements of CH$_4$ fluxes and their isotopic signatures from sites reflecting the diversity of environment of wetlands and inland waters, complemented with environmental metadata (e.g. soil temperature and moisture, vegetation types, water temperature, acidity, nutrient concentrations, net primary productivity, soil carbon density for wetlands, lake morphologies) will allow us to better understand and estimate the processes of production and transport to the atmosphere (diffusive, ebullition, plants mediated, etc.) and to better constrain methane fluxes and their isotopic signatures in the different modelling approaches (Glagolev et al., 2011; Turetsky et al., 2014).

2. *Shortcoming 2.* The uncertainty in the global methane sink from both top-down and bottom-up approaches has not been reduced.

The inverse systems used here have similar caveats to those described in Saunois et al. (2016, 2020) (same OH field, same kind of proxy method to optimise it), leading to quite a constrained atmospheric sink and therefore total global CH$_4$ sources. Although we have used the latest release of CCMI-2022 (Plummer et al., 2021) and CMIP6 simulations (Collins et al., 2017), the uncertainty of derived CH$_4$ chemical loss from the chemistry-climate models remains at the same (large) level compared to the previous intercomparison project ACCMIP (Lamarque et al., 2013). The causes of uncertainties on the CH$_4$ loss and the differences between the different OH fields derived from chemistry-transport models (CTMs) and climate-chemistry models (CCMs) have been widely discussed (e.g. Nicely et al., 2017; Zhao et al., 2019, 2020a). These results emphasise the need to first assess and then improve atmospheric transport and chemistry models, especially vertically, and to integrate robust representation of OH fields in atmospheric models. Recently, numerous efforts based on satellite data have been made to constrain OH distribution, variability, and trends (e.g. Anderson et al., 2023, 2024; Pimlott et al., 2022; Zhao et al., 2023; Zhu et al., 2022). Finally, soil uptake estimates rely on very few studies, and interannual variations remain underconstrained.

Next steps, in the short term, could include developments by the modelling community in the following:

- The soil uptake needs to be estimated with different land surface models (creating an ensemble), and its variations over the past decade need to be discussed.
- The impact of using updated and varying soil uptake estimates needs to be assessed, especially considering a warmer climate in the top-down approach. Indeed, for top-down models resolving for the net flux of CH₄ at the surface integrating a larger estimate of soil uptake would allow larger emissions and then would reduce the uncertainty with the bottom-up estimates of total CH₄ sources.

- The reactivity of the air parcels in the chemistry–climate models needs to be studied further, and new diagnostics need to be defined to assess modelled CH₄ lifetimes such as in Prather et al. (2023).

- Benchmarking of CTMs and CCMs needs to be developed regarding simulated OH distribution and variability (as in Zhao et al. (2019) for example) to increase efforts to assess biases and improve atmospheric chemical schemes in CTMs and CCMs.

- Methods need to be developed to better constrain OH. Some have been proposed: satellite CH₄ observations (Zhang et al., 2018; Anderson et al., 2023, 2024) could afford this, but strategy is needed (see Duncan et al., 2024, and references therein), and using halogenated compounds beyond methyl chloroform (MCF), such as done in box models (Thompson et al., 2024) to derive a 3D dynamical OH. Such methods should be able to reach very low uncertainty for OH burden and trends (< 2%) in order to really better constrain the CH₄ budget. Duncan et al. (2024) discuss the existing satellite-based methods and propose a strategy to constrain OH from space-based approaches.

- The aforementioned different potential OH chemical fields need to be integrated, also including interannual variability, to assess the impact on the methane budget following Zhao et al. (2020a).

In the long run, other parameters should be (better) integrated into top-down approaches:

- The magnitude of the CH₄ loss through oxidation by tropospheric Cl is a process debated in the recent literature. More modelling (e.g. Thanwerdas et al., 2022b) and instrumental studies should be devoted to reducing the uncertainty of this potential additional sink before integrating it in top-down models. This would be especially critical if inversions using ¹³C–CH₄ observations were included in GMB in the future.

3. *Shortcoming 3*. The partitioning of methane sources and sinks by region and emission sectors using top-down approaches are lacking strong constraints.

In this work, we report inversions assimilating satellite data from GOSAT, which bring more constraints than provided

by surface stations alone, especially over tropical continents. However, we still found that satellite- and surface-based inversions and the different inversion systems do not consistently infer the same regional flux distribution.

The estimates contributing to the Global Methane Budget are further used in more specific studies focusing on the comparison of the estimates from bottom-up and top-down approaches at national (Deng et al., 2022) and regional scales, including efforts from the GCP-REgional Carbon Cycle Assessment and Processes (RECCAP2) (Petrescu et al., 2021, 2023; Tibrewal et al., 2024; Lauerwald et al., 2023b; and other RECCAP2 publications to come, see https://www.globalcarbonproject.org/reccap/publications.htm, last access: 1 April 2025).

Next steps, in the short term, could integrate developments to be made by the top-down community:

- GOSAT 2 retrievals should be included (Noël et al., 2022; Imasu et al., 2023) for the GOSAT-based inversions, and TROPOMI-based inversions should be considered (as done in Tsuruta et al., 2023, Shen et al., 2023, Chen et al., 2022, Qu et al., 2021 or Yu et al., 2023) in the next releases once at least 8 years of data are available to provide a decadal estimate and biases are reduced for global-scale use (Lorente et al., 2023; Balasus et al., 2023). Indeed, recent satellite developments have provided higher temporal and spatial resolutions of CH₄ observations in regions with poor in situ measurements (Fig. S9; such as TROPOMI observations in northern Africa).

- Newly available updated gridded products need to be integrated for the different natural sources of CH₄ in their prior fluxes (e.g. inland freshwaters) to reach a full spatial description of sources and sinks and to be able to better compare the top-down budget with the bottom-up budget.

- Newly developed 4D variational inversion systems using isotopic species in the top-down budget need to be integrated (Basu et al., 2022; Thanwerdas et al., 2024; Drinkwater et al., 2023; Mannisenaho et al., 2023).

- The availability of in situ data at high temporal resolution needs to be improved for the scientific community, especially ones covering poorly documented regions such as China (Liu et al., 2021b; Guo et al., 2020), India (Nomura et al., 2021; Lin et al., 2015; Tiwari and Kumar, 2012), and Siberia (Sasakawa et al., 2010, 2017; Fujita et al., 2020; Winderlich et al., 2010), which have not been delivered so far to international databases or only at poor temporal resolution.

- Information from imagery satellites needs to be integrated (e.g. TROPOMI, Carbon Mapper, MethaneSAT, GHGSAT) for high to super-emitters to improve prior
fluxes of anthropogenic emissions in terms of quantity and locations for each covered sector.

In the long run, integrating more measurements and regional studies will help to improve the top-down systems and further reduce the uncertainties:

- CH₄ surface networks need to be extended to poorly observed regions (e.g. tropics, China, India, high latitudes) and to the vertical dimension. Aircraft regular measurements (e.g. Filges et al., 2015; Brenninkmeijer et al., 2007; Paris et al., 2010; Sweeney et al., 2015), AirCore campaigns (e.g. Andersen et al., 2018; Membrive et al., 2017), and TCCON observations (e.g. Wunch et al., 2011, 2019) remain critical in complementing satellite data that do not observe well in cloudy regions and at high latitudes. Those observations are also crucial to evaluate and eventually correct satellite biases (Buchwitz et al., 2022).

- Continuous isotopic measurements of CH₄ need to be extended and developed to help partitioning methane sources and to be integrated in 4D variational isotopic inversions (e.g. Yacovitch et al., 2021).

- Global data from future satellite instruments with intrinsic low biases need to be integrated, such as active lidar techniques with MERLIN (Ehret et al., 2017), which are promising to overcome issues of systematic errors (Bousquet et al., 2018) and should provide measurements over the Arctic, contrary to the existing and planned passive missions.

- Other co-emitted species such as radiocarbon for fossil/non-fossil emissions (Lassey et al., 2007a, 2007b; Petrenko et al., 2017), CO (e.g. Zheng et al., 2019) for biomass burning emissions, and ethane for fugitive emissions (e.g. Ramsden et al., 2022) could bring additional information for partitioning emissions.

4. *Shortcoming*. The atmospheric transport models used in the top-down budget still present issues that have not been fully investigated.

The TRANSCOM experiment synthesised in Patra et al. (2011) showed a large sensitivity of the representation of atmospheric transport on CH₄ abundances in the atmosphere. In particular, the modelled CH₄ budget appeared to depend strongly on the troposphere–stratosphere exchange rate and thus on the model vertical grid structure and circulation in the lower stratosphere. Also, regional changes in the CH₄ budget depend on the characteristics of the atmospheric transport models used in the inversion (Bruhwiler et al., 2017; Locatelli et al., 2015). This axis of research is demanding important development from the atmospheric modelling community. Waiting for future improvements (finer horizontal and vertical resolutions, more accurate physical

parameterisation, increase in computing resources, etc.), assessing atmospheric transport error and the impact on the top-down budget remain crucial and mostly rely on the use of an ensemble of models. Methodology changes should be integrated into the next methane budget releases.

Firstly, independent validation should be performed against aircraft measurements (available through the CH₄ GLOBALVIEWplus v6.0 ObsPack (Schuldt et al., 2023), the IAGOS data portal (https://iagos.aeris-data.fr/download/, last access: 1 April 2025), the NIES portal (https://db.cger.nies.go.jp/ged/en/datasetlist/index.html, last access: 1 April 2025) for CONTRAIL (e.g.Machida et al., 2008) and Siberian measurements (e.g. Sasakiwa et al., 2017), or the WDCGG data portal (https://gaw.kishou.go.jp/, last access: 1 April 2025) for additional flights over three other Japanese airports and Orléans, France). Secondly, AirCore data sets (available through the NOAA Global Monitoring Laboratory website (https://gml.noaa.gov/aftp/data/AirCore/, last access: 1 April 2025, Baier et al., 2021) and the French AirCore programme for atmospheric sampling (https://aircore.aeris-data.fr, last access: 1 April 2025, Membrive et al., 2017)) should be used to evaluate the vertical profile of methane in the models. Then those evaluations against aircraft and AirCore measurements could be used to sort or weight the different estimates in the top-down budget.

Next steps, in the short term, could include some development to be addressed by the top-down community to reduce atmospheric transport errors:

- Further methodologies need to be developed to extract stratospheric partial column abundances from observations such as TCCON data (Saad et al., 2014; Wang et al., 2014), AirCore (e.g. Andersen et al., 2018; Membrive et al., 2017), or ACE-FTS (De Mazière et al., 2008) or MIPAS (Glatthor et al., 2024) satellite data.

- SWIR and TIR measurements need to be combined from space to better constrain the tropospheric column, from TROPOMI and IASI, for example in the Methane-Plus ESA project (https://methaneplus.eu/#docs, last access: 1 April 2025, Buchwitz et al., 2024) or GOSAT (Kuze et al., 2020).

- Transport models codes need to be able to be run on graphics processing units (GPUs) to achieve subdegree-resolution global inversions (Chevallier et al., 2023).

In the long run, there need to be developments within the dynamical core of the atmospheric transport models through the implementation of hexagonal–icosaedric grid with finer resolution (Dubos et al., 2015; Niwa et al., 2017, 2022; Lloret et al., 2023), and improvements in the simulated boundary layer dynamics or troposphere–stratosphere exchanges are promising to reduce atmospheric transport errors.
## 8 Data availability

The data presented here are made available in the belief that their dissemination will lead to greater understanding and new scientific insights into the methane budget and changes to it and help to reduce its uncertainties. For research projects, if the data used are essential to the work to be published or if the conclusion or results largely depend on the data, co-authorship should be considered. Full contact details and information on how to cite the data are given in the accompanying database.

The accompanying database includes a NetCDF file defining the regions used, an archive with the maps of prior fluxes used in the top-down activity, an archive with data corresponding to Figs. 3 and 5, and one Excel file organised in the following spreadsheets.

The file Global_Methane_Budget_2000-2020_v1.0.xlsx includes (1) a summary, (2) the methane observed mixing ratio and growth rate from the four global networks (NOAA, AGAGE, CSIRO and UCI), (3) the evolution of global anthropogenic methane emissions (including biomass burning emissions) used to produce Fig. 2, (4) the global and latitudinal budgets over 2000–2009 based on bottom-up approaches, (5) the global and latitudinal budgets over 2000–2009 based on top-down approaches, (6) the global and latitudinal budgets over 2010–2019 based on bottom-up approaches, (7) the global and latitudinal budgets over 2010–2019 based on top-down approaches, (8) the global and latitudinal budgets for year 2020 based on bottom-up approaches, (9) the global and latitudinal budgets for year 2020 based on top-down approaches, and (10) the list of contributors to contact for further information on specific data.

This database is available from ICOS Carbon Portal (https://doi.org/10.18160/GKQ9-2RHT, Martinez et al., 2024).

## 9 Conclusions

We have built an updated global methane budget by using and synthesising a large ensemble of published methods and new results using a consistent, transparent, and traceable approach, including atmospheric observations and inversions (top-down models), process-based models for land surface emissions and atmospheric chemistry, and inventories of anthropogenic emissions (bottom-up models and inventories). For the 2010–2019 decade, global CH₄ emissions are 575 Tg CH₄ yr⁻¹ (range of 553–586 Tg CH₄ yr⁻¹), as estimated by top-down inversions. About 65 % of global emissions are anthropogenic (range of 63 %–68 %). Bottom-up models and inventories suggest larger global emissions (669 Tg CH₄ yr⁻¹ [512–849]) mostly because of larger and more uncertain natural emissions from inland freshwater systems, natural wetlands, geological seepage, and likely some unresolved double counting of these sources. It is also likely that some of the individual bottom-up emission estimates are

too high, leading to larger global emissions from the bottom-up approach than the atmospheric constraints suggest. However, the important progress in this update is that for the first time, the bottom-up and top-down budgets agree within their uncertainty ranges. This is substantial progress toward defining more accurate global methane emissions.

The latitudinal breakdown inferred from the top-down approach reveals a dominant role of tropical emissions (~64%) compared to mid-latitudes (~32%) and high (~4%) northern latitudes (above 60°N) emissions.

Our results, including an extended set of atmospheric inversions, are compared with the previous budget syntheses of Kirschke et al. (2013) and Saunois et al. (2016, 2020). They show overall good consistency when comparing the same decade (2000–2009) at the global and latitudinal scales. The magnitude and uncertainty of most natural or indirect anthropogenic sources have been revised and updated. In particular, this new budget benefits from large efforts and collaborations from the research community to provide improved estimates of the magnitude and uncertainty of the different freshwater sources and helps reduce the potential double counting at the global scale. Of note, newly available gridded data sets for lakes, ponds, reservoirs, streams, and rivers allow building latitudinal and regional estimates for all these sources for the first time in these estimates. In the next review, we hope to be able to reduce uncertainties in emissions from inland freshwater systems by better quantifying the emission factors of each contributing subsystems (streams, rivers, lakes, ponds) and estimating double counting at regional scale or avoiding double counting by better defining the surface areas of each ecosystem. Another important priority for improvements is the uncertainty on the chemical loss of CH₄, which still needs to be better assessed in both the top-down and the bottom-up budgets. Building on the improvement of the points detailed in Sect. 7, our aim is to update this budget synthesis as a living review paper regularly (~ every 3 or 4 years). Each update will produce a more recent decadal CH₄ budget, highlight changes in emissions and trends, and incorporate newly available data and model improvements.

It is still under debate why exactly there has been a sustained increase in atmospheric CH₄ (more than +5 ppb yr⁻¹) since 2007 (Nisbet et al., 2019; Nisbet et al., 2023; Turner et al., 2019). Some likely explanations, already introduced by Saunois et al. (2017) and further investigated by Jackson et al. (2020, 2024) and other studies, include, by decreasing order of certainty, the following: (1) a positive contribution from microbial and fossil sources (e.g. Nisbet et al., 2019, 2023; Schwietzke et al., 2016; Jackson et al., 2020), a negative contribution from biomass burning emissions before 2014 (Giglio et al., 2013; Worden et al., 2017); (2) a negligible role of Arctic emission changes (e.g. Nisbet et al., 2019; Saunois et al., 2017); and (3) a tropical dominance of the increasing emissions (e.g. Saunois et al., 2017; Jackson et al., 2020; Wilson et al., 2021; Drinkwater et al., 2023). Although the accelerated atmospheric methane growth rate in 2020
(15.2 ppb yr⁻¹) has found some explanation with the im-
pact of the world pandemic in 2020, the sustained observed
growth rates in 2021 (17.8 ppb yr⁻¹) and 2022 (14 ppb yr⁻¹)
still challenge our understanding of the global methane cy-
cle. While in Jackson et al. (2020, 2024) the increase in
CH₄ emissions over the last 2 decades is attributed almost
entirely to direct anthropogenic emissions, the uncertainty
range from the GMB ensemble is large, and the contribution
from natural emissions (wetlands) is still largely uncertain.
Besides the decadal change in CH₄ emissions, large interan-
nual variability can occur from these natural emissions. The
recent high record of CH₄ growth rate highlights the poten-
tial of large variations from natural emissions from one year
to another, in particular wetland emissions (e.g. Peng et al.,
2022; Feng et al., 2023). These remain the challenges to be
overcome in better quantifying global methane emissions.

Further investigation is needed in follow-up studies to
(1) compare these results to the official UNFCCC declara-
tions and to important assessment (as those of IEA) as done
previously for example in Deng et al. (2022, 2024) or more
specifically for fossil fuel emissions in Tibrewal et al. (2024)
and (2) further discuss the trend and interannual variabil-
ity of CH₄ sources and sinks at sectoral and regional scales
as in Jackson et al. (2020, 2024), Stavert et al. (2021), or
RECCAP2-related publications (e.g. Petrescu et al., 2021,
2023; Lauerwald et al., 2023b; Hugelius et al., 2024) and dis-
cuss the compatibility of the budget against the atmospheric
isotopic signal such as in Saunois et al. (2017). The next bud-
gets will be critical to assess whether the Global Methane
Pledge is successful and assess methane mitigation efforts.

The GCP will continue to support and coordinate the de-
velopment of improved flux estimates for all budget compo-
nents and new underlying science to support improved mod-
elling, acquisition of observations, and data integration. At
regular intervals (3–4 years), we will continue to bring all
flux components together to produce an improved and up-
dated global CH₄ budget and provide a global benchmark
for other CH₄ products and assessments.
Appendix A

<table><caption>Table A1. Comparison of terminologies used in this study and previous reports for methane sources.</caption><thead><tr><th>GCP terminology (this study)</th><th>IPCC AR6 (Canadell et al., 2021)</th><th>National GHG inventories (used by UNFCCC according to IPCC (2006) and IPCC (2019))</th><th>IPCC (2006, 2019) source sector numbering</th></tr></thead><tbody><tr><td colspan="4">Anthropogenic sources</td></tr><tr><td rowspan="4">Fossil fuels</td><td>Coal mining</td><td>Coal mining</td><td>Fugitive emissions from fuels/solid fuels</td><td>1B1</td></tr><tr><td>Oil and gas</td><td>Oil and gas</td><td>Fugitive emissions from fuels/oil and natural gas</td><td>1B2</td></tr><tr><td>Transport</td><td>Transport</td><td>Transport</td><td>1A3</td></tr><tr><td>Industry</td><td>Industry</td><td>Mineral, chemical, metal industry, and others<br>Energy/fuel combustion activities</td><td>2A, 2B, 2C, 2D, 2E<br>1A except 1A3 + 1B3</td></tr><tr><td rowspan="2">Agriculture</td><td>Enteric fermentation and manure management</td><td>Enteric fermentation and manure management</td><td>Livestock</td><td>3A</td></tr><tr><td>Rice cultivation</td><td>Rice cultivation</td><td>Rice cultivation</td><td>3C7</td></tr><tr><td>Waste</td><td>Landfills and waste</td><td>Landfills and waste</td><td>Waste</td><td>4</td></tr><tr><td rowspan="2">Biofuel and biomass burning</td><td>Biofuel burning</td><td>Biofuel burning</td><td>Biofuel burning</td><td>1A4b</td></tr><tr><td>Biomass burning</td><td>Biomass burning</td><td>Biomass burning</td><td>3C1</td></tr><tr><td colspan="4">Natural and indirect sources</td></tr><tr><td>Wetlands</td><td>Wetlands</td><td>Wetlands</td><td>–</td><td>–</td></tr><tr><td rowspan="2">Inland freshwaters</td><td>Reservoirs</td><td>Included in inland freshwaters</td><td>Land (including reservoirs)</td><td>in 3B</td></tr><tr><td>Lakes, ponds, and rivers</td><td>Including in inland freshwaters</td><td>Only canal, ditches and ponds for human uses</td><td>in 3B</td></tr><tr><td rowspan="3">Other natural sources</td><td>Oceans</td><td>Oceans</td><td>–</td><td>–</td></tr><tr><td>Termites</td><td>Termites</td><td>–</td><td>–</td></tr><tr><td>Geological sources</td><td>Geological sources</td><td>–</td><td>–</td></tr></tbody></table>
<table><caption>**Table A2.** Summary of methodological changes since the previous budget (Saunois et al., 2020). No significant changes have been applied to the vegetation (Sect. 3.2.8), wild animal (Sect. 3.2.5), and terrestrial permafrost and hydrates (Sect. 3.2.7) estimates, though literature has been expanded and/or updated.</caption><thead><tr><th></th><th>Saunois et al. (2020)</th><th>This study</th></tr></thead><tbody><tr><td>Region definitions (Table S1, Fig. S3)</td><td>18 continental regions + ocean</td><td>same regions except the last region including only Australia and New Zealand and called Australasia</td></tr><tr><td>Anthropogenic global inventories (see Table 1, Sect. 3.1.1)</td><td>CEDS, EDGARv4.3.2, USEPA (2012), FAO, and GAINS ECLIPSE v6</td><td>CEDS, EDGARv6, and v7, USEPA (2019), FAO, IIASA GAINS v4<br>Add estimate of ultra emitters from Lauvaux et al. (2022)</td></tr><tr><td>Biomass burning data sets</td><td>FINNv1.5, GFASv1.3, GFEDv4.1s, QFEDv2.5</td><td>FINNv2.5, GFASv1.3, GFEDv4.1s, QFEDv2.5</td></tr><tr><td>Estimate of wetland emissions (see Tables 2 and S3 and Sect. 3.2.1)</td><td>13 land surface models involved, runs with either prescribed areas or based on hydrological scheme, single meteorological forcing</td><td>16 land surface models involved, runs with either prescribed areas or based on hydrological scheme, two sets of meteorological forcings</td></tr><tr><td>Estimate of reservoir emissions (Sect.3.2.2)</td><td>based on Deemer et al. (2016)</td><td>based on Johnson et al. (2021), Johnson (2021), Rosentreter et al. (2021), and Harrison et al. (2021)</td></tr><tr><td>Estimate of lake and pond emissions (Sect.3.2.2)</td><td>based on Bastviken et al. (2011), Wik et al. (2016b), and Tan and Zhuang (2015)</td><td>lakes &gt; 0.1 km<sup>2</sup>:<br>based on Rosentreter et al. (2021), Zhuang et al. (2023), and Johnson et al. (2022a, b)<br>lakes and ponds &lt; 0.1 km<sup>2</sup>: based on Rosentreter et al. (2021) and Johnson et al. (2022a, b)</td></tr><tr><td>Estimates of stream and river emissions (Sect. 3.2.2)</td><td>from Stanley et al. (2016)</td><td>based on Rosentreter et al. (2021), Rocher-Ros et al. (2023) and Rocher-Ros (2023)</td></tr><tr><td>Estimates of the anthropogenic perturbation component of inland freshwater emissions (Sect. 3.2.2)</td><td>–</td><td>based on several individual studies on the effect of eutrophication on emissions from lakes and ponds (see text in Sect. 3.2.2)</td></tr><tr><td>Estimate of the double counting in the aquatic systems (Sect. 3.2.2)</td><td>–</td><td>due to the counting of small lakes and ponds (&lt; 0.1 km<sup>2</sup>) in the vegetated wetlands areas used in land surface models and to lateral transport from vegetated wetland to rivers.</td></tr><tr><td>Geological sources (Sect. 3.2.3) – onshore and offshore</td><td>based on Etiope and Schwietzke (2019)</td><td>same as in Saunois et al. (2020)</td></tr><tr><td>Termite emissions (Sect. 3.2.4)</td><td>GPP: Zhang et al. (2017)<br>termite biomass: Jung et al. (2011)<br>EF: Kirschke et al. (2013) and Fraser et al., 1986)</td><td>GPP: Wild et al. (2022)<br>termite biomass: based on different studies depending on regions (see text)<br>EF: Sugimoto et al. (1998)<br>Applied a correction factor for mound from Nauer et al. (2018)</td></tr><tr><td>Oceanic sources (Sect. 3.2.6)</td><td>modern biogenic: based on Wuebbles and Hayhoe (2002), Laruelle et al. (2013), and Rosentreter et al. (2018); geological: based on Etiope et al. (2019)</td><td>modern biogenic: based on Rosentreter et al. (2021, 2023) and Laruelle et al. (2025)<br>geological: based on Etiope et al. (2019)</td></tr><tr><td>Tropospheric OH oxidation (Sect. 3.3.2) and stratospheric loss (Sect. 3.3.3) (see Supplement Table S4)</td><td>based on results from 11 models contributing to the Chemistry Climate Model Initiative (Morgenstern et al., 2017)</td><td>based on results from 11 models contributing to the Chemistry Climate Model Initiative 2022 (Plummer et al., 2021) and the CMIP6 simulations (Collins et al., 2017)</td></tr><tr><td>Tropospheric reaction with Cl</td><td>based on Hossaini et al. (2016), Wang et al. (2019b), and Gromov et al. (2018)</td><td>based on Hossaini et al. (2016), Sherwen et al. (2016), Wang et al. (2019b, 2021b), and Gromov et al. (2018)</td></tr><tr><td>Soil uptake (see Table S6)</td><td>based on Tian et al. (2016)</td><td>based on VISIT, JSBACH and MeMo surface models</td></tr><tr><td>Estimates through top-down approaches (see Tables S7 and S8 to S11)</td><td>9 inverse systems contributing, prior fluxes based on EDGARv4.2 or v4.3.2 for most inversions. Most inversion used constant OH.</td><td>7 inverse systems contributing, runs with constant and varying OH, prior fluxes based on either EDGARv6 or GAINS</td></tr></tbody></table>
<table><caption>**Table A3.** Funding supporting the production of the various components of the global methane budget in addition to the authors' supporting institutions (see also acknowledgements).</caption><thead><tr><th>Funder and grant number (where relevant)</th><th>Authors/simulations/observations</th></tr></thead><tbody><tr><td>Director, Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 to Lawrence Berkeley National Laboratory as part of the RUBISCO Scientific Focus Area.</td><td>WJR, QZ, E3SM/ELM simulations</td></tr><tr><td>Funded by NASA's Interdisciplinary Research in Earth Science (IDS) programme and the NASA Terrestrial Ecology and Tropospheric Composition programmes</td><td>MSJ; lake and reservoir bottom-up methane emission data sets</td></tr><tr><td>Funded by Agence National de la Recherche through the project Advanced Methane Budget through Multi-constraints and Multi-data streams Modelling (AMB-M³) – (ANR-21-CE01-0030)</td><td>AM, MS</td></tr><tr><td>Funded by the National Science Foundation (grant no. 2143449)</td><td>MAH</td></tr><tr><td>The Environment Research and Technology Development Fund (JPMEERF21S20800) of the Environmental Restoration and Conservation Agency provided by Ministry of the Environment of Japan</td><td>YN, NISMON-CH₄</td></tr><tr><td>Funded by the German Federal Ministry of Education and Research (BMBF) via the "PalMod" project (grant no. 01LP1921A) and EU H2020 (grant no. 951288 ERC Q-ARCTIC)</td><td>TK; CH₄ emission modelling with JSBACH and LPJ-MPI</td></tr><tr><td>Funded by the Swedish Research Council VR (2020-05338) and Swedish National Space Agency (209/19)</td><td>WZ; LPJ-GUESS simulations</td></tr><tr><td>Funded by BELSPO (project FedTwin ReCAP), EU Horizon 2020 project ESM2025 (no. 101003536), and FRNS PDR project CH₄-lake (T.0191.23)</td><td>PR; inland water, coastal and oceanic CH₄ emission synthesis</td></tr><tr><td>EU H2020 (725546 ERC METLAKE and 101015825 TRIAGE), Swedish Research Councils VR (2022-03841) and Formas (2018-01794)</td><td>DB; inland waters – data and bottom-up estimation.</td></tr><tr><td>Supported by the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)</td><td>NG; JULES simulations</td></tr><tr><td>Funded by United Nations Environment Programme, Stanford University DTIE21-EN3143</td><td>RBJ; inversions and general budget support</td></tr><tr><td>The Joint Fund for Regional Innovation and Development of the National Natural Science Foundation (grant no. U22A20570); the Natural Sciences and Engineering Research Council of Canada (NSERC, grant no. 371706)</td><td>Changhui Peng/TRIPLEX-GHG</td></tr><tr><td>Computing Resources</td><td></td></tr><tr><td>LSCE computing resources</td><td>Marielle Saunois, Philippe Bousquet, Joël Thanwerdas and Adrien Martinez</td></tr><tr><td>NASA High-End Computing (HEC) programme through the NASA Advanced Supercomputing (NAS) Division at NASA Ames Research Center</td><td>Matthew S. Johnson (MSJ)</td></tr><tr><td>Deutsches Klimarechenzentrum (DKRZ), Hamburg, Germany</td><td>Thomas Kleinen (TK)</td></tr><tr><td>ALICE High Performance Computing Facility at the University of Leicester</td><td>GOSAT retrievals</td></tr><tr><td>FUJITSU PRIMERGY CX2550M5 at MRI and NEC SX-Aurora TSUBASA at NIES</td><td>Yosuke Niwa (YN)</td></tr><tr><td>Support for atmospheric observations</td><td></td></tr><tr><td>Australian Antarctic Division</td><td>CSIRO flask network</td></tr><tr><td>Australian Institute of Marine Science</td><td>CSIRO flask network</td></tr><tr><td>Bureau of Meteorology (Australia)</td><td>Kennaook/Cape Grim AGAGE, CSIRO flask network</td></tr><tr><td>Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia)</td><td>Kennaook/Cape Grim AGAGE, CSIRO flask network</td></tr><tr><td>Department of Climate Change, Energy, the Environment and Water (DCCEEW, Australia)</td><td>Kennaook/Cape Grim AGAGE</td></tr><tr><td>Meteorological Service of Canada</td><td>CSIRO flask network</td></tr><tr><td>NASA: grants NAG5-12669, NNX07AE89G, NNX11AF17G, NNX16AC98G and 80NSSC21K1369 to MIT with subawards to the University of Bristol (for Barbados and Mace Head) and CSIRO (for Kennaook/Cape Grim); grants NAG5-4023, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G, NNX16AC96G, NNX16AC97G, 80NSSC21K1210 and 80NSSC21K1201 to SIO.</td><td>AGAGE calibrations and measurements at SIO, La Jolla and AGAGE station operations at Trinidad Head, Mace Head, Barbados, American Samoa, and Kennaook/Cape Grim</td></tr><tr><td>National Oceanic and Atmospheric Administration (NOAA, USA) contract RA133R15CN0008 to the University of Bristol</td><td>Barbados</td></tr><tr><td>NOAA USA</td><td>CSIRO flask network</td></tr><tr><td>Refrigerant Reclaim Australia</td><td>Kennaook/Cape Grim AGAGE</td></tr><tr><td>UK Department for Business, Energy & Industrial Strategy (BEIS) contract TRN1537/06/2018 and TRN 5488/11/2021 to the University of Bristol</td><td>Mace Head</td></tr><tr><td>National Oceanic and Atmospheric Administration (NOAA, USA)</td><td>Cape Matatula</td></tr><tr><td>Japanese Ministry of Environment</td><td>GOSAT data, Robert Parker</td></tr><tr><td>Japanese Aerospace Exploration Agency, National Institute for Environmental Studies</td><td>GOSAT data, Robert Parker</td></tr><tr><td>UKRI UK: grants NE/W004895/1, NE/R016518/1, NE/X019071/1 and MR/X033139/1</td><td>GOSAT data, Robert Parker</td></tr><tr><td>The Swedish Research Council VR (2022-04839), European Space Agency projects AMPAC-Net and CCI+ permafrost, European Union's Horizon 2020 Research and Innovation Programme to the Nunataryuk project (no. 773421)</td><td>Permafrost region, Gustaf Hugelius</td></tr></tbody></table>
**Note on former version.** A former version of this article was published on 15 July 2020 and is available at https://doi.org/10.5194/essd-12-1561-2020.

**Supplement.** The supplement related to this article is available online at https://doi.org/10.5194/essd-17-1873-2025-supplement.

**Author contributions.** MS, AM, and JT gathered the bottom-up and top-down data sets and performed the post-processing and analysis.

MS, BP, PB, PeC, and RJ coordinated the global budget. MS, BP, PB, PeC, RJ, PP, and PCi contributed to the update of the full text, and all coauthors appended comments. AM, ED, and XL produced the figures. DJB, NG, PH, AI, AJ, TK, TL, XL, KMcD, JMe, JMu, SP, CP, WR, HT, YY, WZ, ZZ, Qing Z, Qiuan Z, and Qian-lai Z performed surface land model simulations to compute wetland emissions. GA, DB, SC, BRD, GE, MAH, GH, MSJ, RL, SN, GRR, JAR, EHS, PRa, PRe, and TSW provided data sets useful for natural emission estimates and/or contributed to text on bottom-up natural emissions. LHI, SJS, TNF, GRvW, and MC provided anthropogenic data sets and contributed to the text for this section. AM, JT, PP, DBe, RJ, YN, AS, AT, and BZ performed atmospheric inversions to compute top-down methane emission estimates for sources and sinks. EJD, XL, DRB, PBK, JM, RJP, MR, MS, DWo, and YYo are PIs of atmospheric observations used in top-down inversions and/or contributed the text describing atmospheric methane observations. FD, MS, and JT contributed to the bottom-up chemical sink section by providing data sets, processing data, and/or contributing to the text. FMF provided data for the soil sink.

**Competing interests.** At least one of the (co-)authors is a member of the editorial board of *Earth System Science Data*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** This paper is the result of a collaborative international effort under the umbrella of the Global Carbon Project, a project of Future Earth and a research partner of the World Climate Research Programme (WCRP). We acknowledge all the people and institutions who provided the data used in the global methane budget as well as the institutions funding parts of this effort (see Table A3). We are very grateful for the help provided by Alex Vermeulen in publishing the Global Methane Budget data set on the Integrated Carbon Observation System (ICOS) website. We acknowledge the modelling groups for making their simulations available for this analysis, the joint WCRP Stratosphere-troposphere Processes And their Role in Climate/International Global Atmo-

spheric Chemistry (SPARC/IGAC) Chemistry-Climate Model Initiative (CCMI) for organising and coordinating the model data analysis activity, and the British Atmospheric Data Centre (BADC) for collecting and archiving the CCMI model output. We acknowledge the long-term support provided by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the National Environmental Science Program - Climate Systems Hub to coordinate and support activities of the Global Carbon Project. We are grateful to the Emissions Database for Global Atmospheric Research (EDGAR) team (Monica Crippa, Diego Guizzardi, Frederico Pagani, Manjola Banja, Edwin Schaaf, Marilena Muntean, William Becker and Fabio Monforti-Ferrario) for the work needed to publish the EDGAR greenhouse gas emission data sets used in this work (https://edgar.jrc.ec.europa.eu/, last access: 1 April 2025). We are particularly indebted to the dedicated station/instrumental operators/scientists that have gathered the data and ensured their high quality.

More specifically, we acknowledge Adriana Gomez-Sanabria, for her work on waste sector emissions in the GAINS model; Katherine Jensen, for her contribution to the Surface Water Microwave Product Series SWAMPS; Fortunat Joos, for his contribution to simulations with the Land surface Processes and eXchanges model (LPX Bern); Ray Langenfelds and Elise Guerette, for their contribution to the CSIRO network; Paul Miller, for his contribution to simulations with the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS); Peng Shushi, for his contribution to simulations with the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) model; Shamil Maksyutov, for his contribution to simulations with the inverse model at the National Institute for Environmental Studies (NIES); Isobel Simpson, for her contribution to the University of California Irvine (UCI) network; Paul Steele, for his former contribution to the CSIRO network; Ray Weiss, for his contribution to the Advanced Global Atmospheric Gases Experiment (AGAGE) network; Christine Widenmeyer, for her contribution to the Fire INventory from the National Center for Atmospheric Research (FINN) database; Xiaoming Xu, for his contribution to simulations with the Integrated Science Assessment Model (ISAM); Yuanzhi Yao, for his contribution to simulations with the Dynamic Land Ecosystem Model (DLEM); Diego Guizzardi, for his contribution to EDGAR; Maria Tenkanen, for her contribution to the Carbon Tracker - Europe (CTE) outputs; and Giulia Conchedda, for her contribution to the Food and Agriculture Organization (FAO) database. FAOSTAT data collection, analysis, and dissemination are funded through FAO regular budget funds. The contribution of relevant experts in member countries is gratefully acknowledged. We acknowledge Juha Hatakka from the Finnish Meteorological Institute (FMI) for making methane measurements at the Pallas station and sharing the data with the community. We thank Ariana Sutton-Grier and Lisamarie Windham-Myers for reviewing an earlier version of the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. We warmly thank the three anonymous reviewers for their time and their feedback, which greatly improved the article.

**Financial support.** This research has been supported by the Biological and Environmental Research programme (grant no. DE-AC02-05CH11231), the Agence Nationale de la Recherche (grant
no. ANR-21-CE01-0030), the Environmental Restoration and Conservation Agency (grant no. JPMEERF21S20800), the Bundesministerium für Bildung und Forschung (grant no. 01LP1921A), Veten-skapsrådet (grant nos. 2020-05338 and 2022-03841), the Swedish National Space Agency (grant no. 209/19), the Belgian Federal Science Policy Office (grant no. FedTwin ReCAP), EU Horizon 2020 (grants nos. 101003536, 951288 ERC Q-ARCTIC, 725546 ERC METLAKE, and 101015825 TRIAGE), the Fonds De La Recherche Scientifique – FNRS (grant no. T.0191.23), the Svenska Forskningsrådet Formas (grant no. 2018-01794), the United Nations Environment Programme (grant no. DTIE21-EN3143), and the Natural Sciences and Engineering Research Council of Canada (grant no. NSERC, no. 371706). Atul K. Jain is supported by the NASA LCLUC programme (award no. 80NSSC24K0920). All funding and support are detailed in Table 3.

**Review statement.** This paper was edited by Yuyu Zhou and reviewed by three anonymous referees.

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