Global Methane Budget 2000–2020

Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Josep Canadell, Robert Jackson, Prabir Patra, Philippe Bousquet, et al.

► To cite this version:

Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, et al. Global Methane Budget 2000–2020. Earth System Science Data, 2025, 17 (5), pp.1873-1958. 10.5194/essd-17-1873-2025. doi:10.5194/essd-17-1873-2025

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Global Methane Budget 2000–2020

Marielle Saunois<fnref n="1" />, Adrien Martinez<fnref n="1" />, Benjamin Poulter<fnref n="2" />, Zhen Zhang<fnref n="3" />,<fnref n="4" />, Peter A. Raymond<fnref n="5" />,
Pierre Regnier<fnref n="6" />, Josep G. Canadell<fnref n="7" />, Robert B. Jackson<fnref n="8" />, Prabir K. Patra<fnref n="9" />,<fnref n="10" />, Philippe Bousquet<fnref n="1" />,
Philippe Ciais<fnref n="1" />, Edward J. Dlugokencky<fnref n="11" />, Xin Lan<fnref n="11" />,<fnref n="12" />, George H. Allen<fnref n="13" />, David Bastviken<fnref n="14" />,
David J. Beerling¹⁵, Dmitry A. Belikov<fnref n="16" />, Donald R. Blake<fnref n="17" />, Simona Castaldi<fnref n="18" />, Monica Crippa<fnref n="19" />,
Bridget R. Deemer<fnref n="20" />, Fraser Dennison<fnref n="21" />, Giuseppe Etiope<fnref n="22" />,²³, Nicola Gedney²⁴,
Lena Höglund-Isaksson²⁵, Meredith A. Holgerson²⁶, Peter O. Hopcroft²⁷, Gustaf Hugelius²⁸,
Akihiko Ito²⁹, Atul K. Jain³⁰, Rajesh Janardanan³¹, Matthew S. Johnson³², Thomas Kleinen³³,
Paul B. Krummel<fnref n="21" />, Ronny Lauerwald³⁴, Tingting Li³⁵, Xiangyu Liu³⁶, Kyle C. McDonald³⁷,
Joe R. Melton³⁸, Jens Mühle³⁹, Jurek Müller⁴⁰, Fabiola Murguia-Flores⁴¹, Yosuke Niwa³¹,⁴²,
Sergio Noce⁴³, Shufen Pan⁴⁴, Robert J. Parker⁴⁵, Changhui Peng⁴⁶,⁴⁷, Michel Ramonet<fnref n="1" />,
William J. Riley⁴⁸, Gerard Rocher-Ros⁴⁹, Judith A. Rosentreter⁵⁰, Motoki Sasakiwa³¹, Arjo Segers⁵¹,
Steven J. Smith⁵²,⁵³, Emily H. Stanley⁵⁴, Joël Thanwerdas⁵⁵,ᵃ, Hanqin Tian⁵⁶, Aki Tsuruta⁵⁷,
Francesco N. Tubiello⁵⁸, Thomas S. Weber⁵⁹, Guido R. van der Werf⁶⁰, Douglas E. J. Worthy⁶¹, Yi Xi<fnref n="1" />,
Yukio Yoshida³¹, Wenxin Zhang⁶²,⁶³, Bo Zheng⁶⁴,⁶⁵, Qing Zhu⁴⁸, Qiuan Zhu⁶⁶, and Qianlai Zhuang³⁶

<footnote n="1">¹Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France</footnote>

<footnote n="2">²NASA Goddard Space Flight Center, Biospheric Science Laboratory, Greenbelt, MD 20771, USA</footnote>

<footnote n="3">³State Key Laboratory of Tibetan Plateau Earth System, Environment and Resource (TPESER), Institute of
Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China</footnote>

<footnote n="4">⁴Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA</footnote>

<footnote n="5">⁵Yale School of the Environment, Yale University, New Haven, CT 06511, USA</footnote>

<footnote n="6">⁶Department Geoscience, Environment & Society (BGEOSYS), Université Libre de Bruxelles,
1050 Brussels, Belgium</footnote>

<footnote n="7">⁷Global Carbon Project, CSIRO Environment, Canberra, ACT 2601, Australia</footnote>

<footnote n="8">⁸Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy,
Stanford University, Stanford, CA 94305-2210, USA</footnote>

<footnote n="9">⁹Research Institute for Global Change, JAMSTEC, 3173-25 Showa-machi,
Kanazawa, Yokohama, 236-0001, Japan</footnote>

<footnote n="10">¹⁰Research Institute for Humanity and Nature, Kyoto 6038047, Japan</footnote>

<footnote n="11">¹¹National Oceanic and Atmospheric Administration Global Monitoring Laboratory (NOAA/GML), 325 Broadway R/GML, Boulder, CO 80305, USA</footnote>

<footnote n="12">¹²Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder,
Boulder, CO 80309, USA</footnote>

<footnote n="13">¹³Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA</footnote>

<footnote n="14">¹⁴Department of Thematic Studies – Environmental Change, Linköping University, 581 83 Linköping, Sweden
¹⁵School of Biosciences, University of Sheffield, Sheffield, S10 2TN, UK</footnote>

<footnote n="16">¹⁶Center for Environmental Remote Sensing, Chiba University, Chiba, 263-8522, Japan</footnote>

<footnote n="17">¹⁷Department of Chemistry, University of California Irvine, 570 Rowland Hall, Irvine, CA 92697, USA</footnote>

<footnote n="18">¹⁸Dipartimento di Scienze Ambientali, Biologiche e Farmaceutiche, Università degli Studi della Campania
Luigi Vanvitelli, via Vivaldi 43, 81100 Caserta, Italy</footnote>

<footnote n="19">¹⁹European Commission, Joint Research Centre (JRC), Ispra, Italy</footnote>

<footnote n="20">²⁰U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, USA</footnote>

<footnote n="21">²¹CSIRO Environment, Aspendale, Victoria 3195, Australia</footnote>

<footnote n="22">²²Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma 2, via V. Murata 605, 00143 Rome, Italy</footnote>
<sup>23</sup>Faculty of Environmental Science and Engineering, Babes-Bolyai University, Cluj-Napoca, Romania

<sup>24</sup>Met Office Hadley Centre, Joint Centre for Hydrometeorological Research, Maclean Building, Wallingford, OX10 8BB, UK

<sup>25</sup>Pollution Management Group (PM), International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria

<sup>26</sup>Department of Ecology & Evolutionary Biology, Cornell University, Ithaca, NY, USA

<sup>27</sup>School of Geography, Earth & Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK

<sup>28</sup>Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden

<sup>29</sup>Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan

<sup>30</sup>Department of Climate, Meteorology and Atmospheric Sciences (CLIMAS), University of Illinois, Urbana-Champaign, Urbana, IL 61801, USA

<sup>31</sup>Earth System Division, National Institute for Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan

<sup>32</sup>Earth Science Division, NASA Ames Research Center, Moffett Field, CA, USA

<sup>33</sup>Max Planck Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany

<sup>34</sup>Université Paris-Saclay, INRAE, AgroParisTech, UMR EcoSys, Palaiseau, France

<sup>35</sup>LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, 100029 Beijing, China

<sup>36</sup>Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA

<sup>37</sup>Department of Earth and Atmospheric Sciences, City College of New York, City University of New York, NY, USA

<sup>38</sup>Climate Research Division, Environment and Climate Change Canada, Victoria, BC, V8W 2Y2, Canada

<sup>39</sup>Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA

<sup>40</sup>Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Sidlerstr. 5, 3012 Bern, Switzerland

<sup>41</sup>Instituto de Investigaciones en Ecología y Sustentabilidad, Universidad Nacional Autónoma de México, Morelia, Mexico

<sup>42</sup>Department of Climate and Geochemistry Research, Meteorological Research Institute (MRI), Nagamine 1-1, Tsukuba, Ibaraki 305-0052, Japan

<sup>43</sup>CMCC Foundation – Euro-Mediterranean Center on Climate Change, Via Igino Garbini, 51, 01100 Viterbo VT, Italy

<sup>44</sup>Department of Engineering and Environmental Studies Program, Boston College, Chestnut Hill, MA 02467, USA

<sup>45</sup>National Centre for Earth Observation, School of Physics and Astronomy, University of Leicester, Leicester, LE1 7RH, UK

<sup>46</sup>Department of Biology Sciences, Institute of Environment Science, University of Quebec at Montreal, Montréal, QC H3C 3P8, Canada

<sup>47</sup>School of Geographic Sciences, Hunan Normal University, 410081 Changsha, China

<sup>48</sup>Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720, USA

<sup>49</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, 90183 Umeå, Sweden

<sup>50</sup>Faculty of Science and Engineering, Southern Cross University, Lismore, NSW 2480, Australia

<sup>51</sup>TNO, Department of Climate Air & Sustainability, P.O. Box 80015, NL-3508-TA, Utrecht, the Netherlands

<sup>52</sup>Joint Global Change Research Institute, Pacific Northwest National Lab, College Park, MD, USA

<sup>53</sup>Center for Global Sustainability, University of Maryland, College Park, MD, USA

<sup>54</sup>Center for Limnology, University of Wisconsin–Madison, Madison, WI, USA

<sup>55</sup>Empa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

<sup>56</sup>Center for Earth System Science and Global Sustainability, Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, USA

<sup>57</sup>Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki, Finland

<sup>58</sup>Statistics Division, Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla, Rome 00153, Italy

<sup>59</sup>Department of Earth and Environmental Sciences, University of Rochester, Rochester, NY 14627, USA
<sup>60</sup>Meteorology and Air Quality Group, Wageningen University and Research, Wageningen, the Netherlands

<sup>61</sup>Environment and Climate Change Canada, 4905, Dufferin Street, Toronto, Canada

<sup>62</sup>School of Geographical and Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, UK

<sup>63</sup>Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, 223 62, Lund, Sweden

<sup>64</sup>Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055 Shenzhen, China

<sup>65</sup>State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, 100084 Beijing, China

<sup>66</sup>College of Geography and Remote Sensing, Hohai University, 210098 Nanjing, China

<sup>a</sup>formerly at: Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRS-UVSQ), Université Paris-Saclay, 91191 Gif-sur-Yvette, France

**Correspondence:** Marielle Saunois (marielle.saunois@lsce.ipsl.fr)

Received: 2 May 2024 – Discussion started: 6 June 2024

Revised: 22 January 2025 – Accepted: 3 February 2025 – Published: 9 May 2025

**Abstract.** Understanding and quantifying the global methane (CH₄) budget is important for assessing realistic pathways to mitigate climate change. CH₄ is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO₂), and both emissions and atmospheric concentrations of CH₄ have continued to increase since 2007 after a temporary pause. The relative importance of CH₄ emissions compared to those of CO₂ for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH₄ sources and from the uncertain magnitude and temporal change in the destruction of CH₄ by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH₄ budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH₄ budget, integrating results of top-down CH₄ emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020.

The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH₄ yr⁻¹). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH₄ yr⁻¹ for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches.

For the 2010–2019 decade, global CH₄ emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH₄ yr⁻¹ (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH₄ yr⁻¹ or ~65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH₄ yr⁻¹ or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH₄ yr⁻¹ (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH₄ yr⁻¹ (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH₄ emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH₄ yr⁻¹) larger global emissions (669 Tg CH₄ yr⁻¹, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the
top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH₄ yr⁻¹ in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH₄ budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters.

The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH₄ yr⁻¹ based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (~25 %). The total sink of CH₄ is estimated at 633 [507–796] Tg CH₄ yr⁻¹ by the bottom-up approaches and at 554 [550–567] Tg CH₄ yr⁻¹ by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified.

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.

We identify five major priorities for improving the CH₄ budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH₄ based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH₄ observations at local (e.g. FLUXNET-CH₄ measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024).

# 1 Introduction

The average surface dry air mole fraction of atmospheric methane (CH₄) reached 1912 ppb in 2022 (Fig. 1; Lan et al., 2024), 2.6 times greater than its estimated pre-industrial value in 1750. This increase is attributable in large part to increased anthropogenic emissions arising primarily from agriculture (e.g. livestock production, rice cultivation, biomass burning), fossil fuel production and use, waste disposal, and alterations to natural CH₄ fluxes due to increased atmospheric CO₂ concentrations, land use (Woodward et al., 2010, Fluet-Chouinard et al., 2023), and climate change (Ciais et al., 2013; Canadell et al., 2021). An equal mass of CH₄ emissions has a stronger impact on climate than carbon dioxide (CO₂), which is reflected by its global warming potential (GWP) relative to CO₂ on a given time horizon. For a 100-year time horizon the GWP of CH₄ emitted by fossil sources is 29.8 (GWP of CH₄ emitted by microbial sources is 27), whereas the values reach 82.5 over a 20-year horizon for CH₄ emitted by fossil sources and 79.7 for CH₄ emitted by microbial sources (Forster et al., 2021). Although global anthropogenic emissions of CH₄ are estimated at around 359 Tg CH₄ yr⁻¹ (Saunois et al., 2020), represent-

ing around 2.5 % of the global CO₂ anthropogenic emissions when converted to units of carbon mass flux for the recent decade, the emissions-based effective radiative forcing of CH₄ concentrations contributed ~31 % (1.19 W m⁻²) to the additional radiative forcing from anthropogenic emissions of greenhouse gases and their precursors (3.84 W m⁻²) over the industrial era (1750–2019) (Forster et al., 2021). Changes in other chemical compounds such as nitrogen oxides (NOₓ) or carbon monoxide (CO) also influence atmospheric CH₄ through changes to its atmospheric lifetime. Emissions of CH₄ contribute to the production of ozone, stratospheric water vapour, and CO₂, and most importantly they affect its own lifetime (Myhre et al., 2013; Shindell et al., 2012). CH₄ has a short lifetime in the atmosphere (about 9 years for the year 2010; Prather et al., 2012; Szopa et al., 2021). Hence a stabilisation or reduction of CH₄ emissions leads to the stabilisation or reduction of its atmospheric concentration (assuming no change in the chemical oxidants), and therefore its radiative forcing, in only a few decades. While reducing CO₂ emissions is necessary to stabilise long-term warming, reducing CH₄ emissions is recognised as an effective option to limit climate warming in the near-term future (Shindell et al.,
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Globally averaged atmospheric CH₄ concentrations (ppb) (a) and annual growth rates G<sub>ATM</sub> (ppb yr<sup>-1</sup>) (b) between 1983 and 2022, from four measurement programmes: National Oceanic and Atmospheric Administration (NOAA), Advanced Global Atmospheric Gases Experiment (AGAGE), Commonwealth Scientific and Industrial Research Organisation (CSIRO), and University of California, Irvine (UCI). Detailed descriptions of methods are given in the supplementary material of Kirschke et al. (2013).</figcaption></figure>

2012; Jackson et al., 2020; Ocko et al., 2021; UNEP, 2021) because of its shorter lifetime compared to CO<sub>2</sub>.

The momentum around the potential of CH₄ to limit near-
term warming led to the launch of the Global Methane Pledge
at the November 2021 Conference of the Parties (COP 26).
Signed by 158 countries (update on October 2024), this col-
lective effort aims at reducing global CH₄ anthropogenic
emissions by at least 30% from 2020 levels by 2030 (Global
Methane Pledge, 2023). Given that global baseline CH₄
emissions are expected to grow through 2030 (by an addi-
tional 20–50 Mt of CH₄, UNEP, 2022), the CH₄ emission
reductions currently needed to reach the Global Methane
Pledge objective (UNEP, 2022) correspond to 36% of the
projected baseline emissions in 2030 (i.e. if no further emis-
sion reductions were implemented). This implies that large
reductions of CH₄ emissions are needed to meet the Global
Methane Pledge, which is also consistent with the 1.5–2 °C
target of the Paris Agreement (UNEP, 2022). Moreover, be-
cause CH₄ is a precursor of important air pollutants such as
ozone, CH₄ emissions reductions are required by two inter-
national conventions – the United Nations Framework Con-
vention on Climate Change (UNFCCC) and the Convention
on Long-Range Transport of Air Pollution (CLRTAP) – mak-
ing this global CH₄ budget assessment all the more critical.

Changes in the magnitude and temporal variation (an-
nual to interannual) in CH₄ sources and sinks over the
past decades are characterised by large uncertainties (e.g.
Kirschke et al., 2013; Saunois et al., 2017; Turner et al.,
2019). Also, the decadal budget suggests relative uncertain-

ties (hereafter reported as min–max ranges) of 20%–35% for inventories of anthropogenic emissions in specific sectors (e.g. agriculture, waste, fossil fuels; Tibrewal et al., 2024), 50% for biomass burning and natural wetland emissions, and up to 100% for other natural sources (e.g. inland waters, geological sources). The uncertainty in the chemical loss of CH₄ by OH, the predominant sink of atmospheric CH₄, has been estimated using Prather et al. (2012) and Rigby et al. (2017). The former study estimated this uncertainty at ~10% from the uncertainty in the reaction rate between CH₄ and OH, and the latter study was based on methyl-chloroform measurements. Bottom-up approaches (chemistry transport models) estimate the uncertainty of the chemical loss by OH at around 15%–20% (Saunois et al., 2016, 2020). This uncertainty in the OH-induced loss translates, in the top-down methods, into the minimum relative uncertainty associated with global CH₄ emissions, as other CH₄ sinks (atomic oxygen and chlorine oxidations, soil uptake) are much smaller and the atmospheric growth rate is well defined (Dlugokencky et al., 2009). Globally, the contribution of natural CH₄ emissions to total emissions can be quantified by combining lifetime estimates with reconstructed pre-industrial atmospheric CH₄ concentrations from ice cores (assuming natural emissions have not been perturbed during the Anthropocene) (e.g. Ehhalt et al., 2001). Regionally or nationally, uncertainties in emissions may reach 40%–60% (e.g. for South America, Africa, China, and India; see Saunois et al., 2016). Another difficulty of the CH₄ budget lies in the necessity to also match the isotopic signal and in particular reflect the decreasing methane isotopic signal ¹³C (Nisbet et al., 2016, 2019). The previous budgets were tested against the isotopic observations (Saunois et al., 2017) and follow an exhaustive assessment (Zhang et al., 2021b). To date only a couple of atmospheric inverse systems are able to assimilate both CH₄ mixing ratios and stable isotopic signal to retrieve fluxes at the global scale (Thanwerdas et al., 2024; Basu et al., 2022), but these systems still need improvements in terms of configuration set-up and computing time resources, in addition to characterisation of source signatures and chemical kinetic effect (Chandra et al., 2024). We hope to be able to report isotopic constrained budgets in the coming years or at least test the budget against the isotopic balance.

To monitor emission reductions, for example to help con-
duct the Paris Agreement’s stocktake, sustained and long-
term monitoring of anthropogenic emissions per sector is
needed in particular for hotspots of emissions that may be
missed in inventories (Bergamaschi et al., 2018; Pacala,
2010; Lauvaux et al., 2022). At the same time, reducing un-
certainties in all individual CH₄ sources, and thus in the over-
all CH₄ budget, remains challenging for at least four reasons.
First, CH₄ is emitted by multiple processes, including natu-
ral and anthropogenic sources, point and diffuse sources, and
sources associated with at least three different production
origins (i.e. microbial, thermogenic, and pyrogenic). These
multiple sources and processes require the integration of data
from diverse scientific communities and across multiple tem-
poral and spatial scales. The production of accurate bottom-
up estimates is complicated by the fact that anthropogenic
emissions result from leakage from fossil fuel production
with large differences between countries depending on tech-
nologies and practices, the fact that many large leak events
are sporadic, and the location of many emissions hotspots is
not well known, and from uncertain emission factors used
to summarise complex microbial processes in the agriculture
and waste sectors. For the latter, examples include difficulties
in upscaling methane emissions from livestock without con-
sidering the variety of animal weight, diet, and environment
and difficulties in assessing emissions from landfills depend-
ing on waste type and waste management technology. Sec-
ond, atmospheric CH₄ is removed mainly by chemical reac-
tions in the atmosphere involving OH and other radicals that
have very short lifetimes (typically ~1 s). Due to the short
lifetime of OH, the spatial and temporal distributions of OH
are highly variable. While OH can be measured locally, cal-
culating global CH₄ loss through OH measurements requires
high-resolution global OH measurements (typically half an
hour to integrate cloud cover and 1 km spatially to consider
OH high reactivity and heterogeneity), which is impossible
from direct OH observations. As a result, OH can only be
calculated through large-scale atmospheric chemistry mod-
elling. Those simulated OH concentrations from transport-
chemistry models prescribed with emissions of precursor
species affecting OH still show uncertain spatiotemporal dis-
tribution from regional to global scales (Zhao et al., 2019).
Third, only the net CH₄ budget (sources minus sinks) is well
constrained by precise observations of atmospheric growth
rates (Dlugokencky et al., 2009), leaving the sum of sources
and the sum of sinks uncertain. One distinctive feature of
CH₄ sources compared to CO₂ fluxes is that the oceanic con-
tribution to the global CH₄ budget is small (~1%-3%),
making CH₄ source estimation predominantly a terrestrial
endeavour (USEPA, 2010b). Finally, we lack comprehen-
sive observations to constrain (1) the areal extent of different
types of wetlands and inland freshwater (Kleinen et al., 2012,
2020, 2021, 2023; Stocker et al., 2014; Zhang et al., 2021a),
(2) models of wetland and inland freshwater emission rates
(Melton et al., 2013; Poulter et al., 2017; Wania et al., 2013;
Bastviken et al., 2011; Wik et al., 2016a; Rosentreter et al.,
2021; Bansal et al., 2023; Lauerwald et al., 2023a; Stan-
ley et al., 2023), (3) inventories of anthropogenic emissions
(Höglund-Isaksson et al., 2020; Crippa et al., 2023; USEPA,
2019), and (4) atmospheric inversions, which aim to estimate
CH₄ emissions from global to regional scales (Houweling et
al., 2017; Jacob et al., 2022).

The global CH₄ budget inferred from atmospheric obser-
vations by atmospheric inversions relies on regional con-
straints from atmospheric sampling networks, which are rel-
atively dense for northern mid-latitudes, with various high-
precision and high-accuracy surface stations, but are sparser
at tropical latitudes and in the Southern Hemisphere (Dlugo-

kencky et al., 2011). Recently, the density of atmospheric ob-
servations has increased in the tropics due to satellite-based
platforms that provide column-averaged CH₄ mixing ratios.
Despite continuous improvements in the precision and ac-
curacy of space-based measurements (e.g. Buchwitz et al.,
2016), systematic errors greater than several parts per bil-
lion on total column observations can still limit the usage of
such data to constrain surface emissions (e.g. Jacob et al.,
2022). The development of robust bias corrections on exist-
ing data can help overcome this issue (e.g. Inoue et al., 2016,
Lorente et al., 2023; Balasus et al., 2023), and satellite data
are now widely used in atmospheric inversions where they
provide more global information on the distribution of fluxes
and highly complement the surface networks (e.g. Lu et al.,
2021).

In this context, the Global Carbon Project (GCP) seeks
to develop a complete picture of the carbon cycle by es-
tablishing common, consistent scientific knowledge to sup-
port policy development and actions to mitigate green-
house gas emissions to the atmosphere (https://www.
globalcarbonproject.org, last access: 1 April 2025). The ob-
jectives of this paper are (1) to analyse and synthesise the cur-
rent knowledge of the global CH4 budget, (2) to better under-
stand and quantify the main robust features of this budget and
its remaining uncertainties, and (3) to make recommenda-
tions for improvement. We combine results from a large en-
semble of bottom-up approaches (e.g. process-based models
for natural wetlands, data-driven approaches for other natural
sources, inventories of anthropogenic emissions and biomass
burning, and atmospheric chemistry models) and top-down
approaches (including CH4 atmospheric observing networks,
atmospheric inversions inferring emissions and sinks from
the assimilation of atmospheric observations into models of
atmospheric transport and chemistry). The focus of this work
is to update the previous assessment made for the period
2000–2017 (Saunois et al., 2020) to the more recent 2000–
2020 period. More in-depth analyses of trends and year-to-
year changes are left to future publications. Our current paper
is a living review, published at about 4-year intervals, to pro-
vide an update and new synthesis of available observational,
statistical, and model data for the overall CH4 budget and its
individual components.

Kirschke et al. (2013) carried out the first CH₄ budget
synthesis, followed by Saunois et al. (2016) and Saunois et
al. (2020), with companion papers by Stavert et al. (2021)
on regional CH₄ budgets and Jackson et al. (2020) focusing
on the last year of the budget (2017). Saunois et al. (2020)
covered 2000–2017 and reported CH₄ emissions and sinks
for three time periods: (1) the latest calendar decade at that
time (2000–2009), (2) data for the latest available decade
(2008–2017), and (3) the latest available year (2017) at the
time. Here, the Global Methane Budget (GMB) covers 2000–
2020 split into the 2000–2009 decade, the 2010–2019 decade
(where data are available), the year 2020 affected by COVID-
induced changes in human activity, and briefly for 2021–
2023 as per data availability (Sect. 6). The CH₄ budget is presented at global, latitudinal, and regional scales, and data can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024). A global, regional, and sectoral assessment of methane emission changes over the last 2 decades is discussed in Jackson et al. (2024) based on the data of Martinez et al. (2024).

Six sections follow this introduction. Section 2 presents the methodology used in the budget – units, definitions of source categories, regions, and data analysis – and discusses the delay between the period of study of the budget and the release date. Section 3 presents the current knowledge about CH₄ sources and sinks based on the ensemble of bottom-up approaches reported here (models, inventories, data-driven approaches). Section 4 reports atmospheric observations and top-down atmospheric inversions gathered for this paper. Section 5, based on Sects. 3 and 4, provides the updated analysis of the global CH₄ budget by comparing bottom-up and top-down estimates and highlighting differences. Section 6 discusses the recent changes in atmospheric CH₄ in relation to changes in CH₄ sources and sinks. Finally, Sect. 7 discusses future developments, missing components, and the most critical remaining uncertainties based on our update to the global CH₄ budget. For easier reading, the list of content of this article is presented in the first section (Sect. S1) of the Supplement.

# 2 Methodology

## 2.1 Units used

Unless specified, fluxes are expressed in teragrams of CH₄ per year (1 Tg CH₄ yr⁻¹ = 10¹² g CH₄ yr⁻¹), while atmospheric mixing ratios are expressed as dry air mole fractions, in parts per billion (ppb), with atmospheric CH₄ annual increases, G_ATM (expressed in ppb yr⁻¹). In the tables, we present mean values and ranges for the two decades 2000–2009 and 2010–2019, together with results for the most recent available year (2020). Results obtained from previous syntheses (i.e. Saunois et al., 2020, and Saunois et al., 2016) are also given for the decade 2000–2009. Following Saunois et al. (2016) and considering that the number of studies is often relatively small for many individual source and sink estimates, uncertainties are reported as minimum and maximum values of the available studies, given in brackets. In doing so, we acknowledge that we do not consider the uncertainty of the individual estimates, and we express uncertainty as the range of available mean estimates, i.e. differences across measurements and methodologies considered. These minimum and maximum values are those presented in Sect. 2.5 and exclude identified outliers.

The CH₄ emission estimates are provided with up to three significant digits, for consistency across all budget flux components and to ensure the accuracy of aggregated fluxes. Nonetheless, given the values of the uncertainties in the CH₄

budget, we encourage the reader to consider no more than two digits as significant for the global total budget.

## 2.2 Period of the budget and availability of data

The bottom-up estimates rely on global anthropogenic emission inventories, an ensemble of process-based models for wetland emissions, and published estimates in the literature for other natural sources. The global gridded anthropogenic inventories (see Sect. 3.1.1) are updated irregularly, generally every 3 to 5 years. The last reported years of available inventories were 2018 or 2019 when we started the top-down modelling activity. In order to cover the period 2000–2020, it was necessary to extrapolate the anthropogenic inventory EDGARv6 (Crippa et al., 2021) to 2020 to use it as prior information for the anthropogenic emissions in the atmospheric inversion systems as explained in the Supplement (Sect. S4). However, EDGARv7 (EDGAR, 2022; Crippa et al., 2023) spanning until 2021 was then released and was used for the bottom-up budget. EDGARv8 (EDGAR, 2023; Crippa et al., 2023), spanning until 2022 and released in 2024, is used in Sect. 6 to discuss the post-2020 methane budget. The land surface (wetland) models were run over the full period 2000–2020 using dynamical wetland areas, derived by remote sensing data or other models of flooded area variability (Sect. 3.2.1).

The atmospheric inversions run until mid-2021, but the last year of reported inversion results is 2020, which represents a 3-year lag with the present. This is due to the long time period it takes to acquire atmospheric in situ data and integrate models. Even though satellite observations are processed operationally and are generally available with a latency of days to weeks, by contrast surface observations can lag from months to years because of the time for flask analyses and data quality checks in (mostly) non-operational chains. In addition, the final 6 months of inversions must be generally ignored because the estimated fluxes are not constrained by as many observations as the previous periods. Lastly, this budget presents an extended synthesis of the most recent development regarding inland water emissions (Sect. 3.2.2) and corrections associated with double counting with wetlands.

## 2.3 Definition of regions

Geographically, emissions are reported globally and for three latitudinal bands (90° S–30° N, 30–60° N, 60–90° N, only for gridded products). When extrapolating emission estimates forward in time (see Sect. 3.1.1), and for the regional budget presented by Stavert et al. (2021), a set of 19 regions (oceans and 18 continental regions; see Fig. S3 in the Supplement) were used. As anthropogenic emissions are often reported by country, we define these regions based on a country list (Table S1 in the Supplement). This approach was compatible with all top-down and bottom-up approaches considered.
The number of regions was chosen to be close to the widely used Transcom intercomparison map (Gurney et al., 2004) but with subdivisions to separate the contributions from important countries or regions for the CH₄ cycle (China, South Asia, tropical America, tropical Africa, the USA, and Russia). The resulting region definition is the same as that used for the Global Carbon Project (GCP) N₂O budget (Tian et al., 2020). Compared to Saunois et al. (2020), the Oceania region has been replaced by Australasia, including only Australia and New Zealand. Other territories formerly in Oceania were included in Southeast Asia.

## 2.4 Definition of source and sink categories

CH₄ is emitted by different processes (i.e. biogenic, thermogenic, or pyrogenic) and can be of anthropogenic or natural origin. Biogenic CH₄ is the final product of the decomposition of organic matter by methanogenic Archaea in anaerobic environments, such as water-saturated soils, swamps, rice paddies, marine and freshwater sediments, landfills, sewage and wastewater treatment facilities, or inside animal digestive systems. Thermogenic methane is formed on geological timescales by the breakdown of buried organic matter due to heat and pressure deep in the Earth's crust. Thermogenic CH₄ reaches the atmosphere through marine and land geological gas seeps. These CH₄ emissions are increased by human activities, for instance, the exploitation and distribution of fossil fuels. Pyrogenic CH₄ is produced by the incomplete combustion of biomass and other organic materials. Peat fires, biomass burning in deforested or degraded areas, wildfires, and biofuel burning are the largest sources of pyrogenic CH₄. CH₄ hydrates, ice-like cages of frozen CH₄ found in continental shelves and slopes and below subsea and land permafrost, can be of either biogenic or thermogenic origin. Each of these three process categories has both anthropogenic and natural components.

In the following, we present the different CH₄ sources depending on their anthropogenic or natural origin, which is relevant to climate policy. Compared to the previous budgets, marginal changes have been made regarding source categories (naming and grouping), to reflect the improved estimates for inland water sources and their indirect anthropogenic component. In the previous Global Methane Budget articles (Saunois et al., 2016, 2020), natural and anthropogenic emissions were split in a way that did not correspond exactly to the definition used by the UNFCCC following the IPCC guidelines (IPCC, 2006), where, for pragmatic reasons, all emissions from managed land are typically reported as anthropogenic. For instance, we considered all wetlands to be natural emissions, despite some wetlands being on managed land and their emissions being partly reported as anthropogenic in UNFCCC national communications. Separating natural from anthropogenic sources could be quite challenging, especially over regions where sources overlap, such as over heavily human-dominated floodplain

deltas for example. The human-induced perturbation of climate, atmospheric CO₂, and nitrogen and sulfur deposition may also cause changes in wetland sources we classified as natural. Following our previous definition, emissions from wetlands, inland freshwaters, thawing permafrost, or geological leaks are accountable for “natural” emissions, even though we acknowledge that climate change and other human perturbations (e.g. eutrophication) may cause changes in those emissions. CH₄ emissions from reservoirs were also considered natural even though reservoirs are human-made. Indeed, since the 2019 refinement to the IPCC guidelines (IPCC, 2019), emissions from reservoirs and other flooded lands have been considered to be anthropogenic by the UNFCCC and should be reported as such. However, these estimates are not provided by inventories and not systematically reported by all countries (especially non-Annex-I countries). In this budget we rename “natural sources” to “natural and indirect anthropogenic sources” to acknowledge that CH₄ emissions from reservoirs, as well as from water bodies that were perturbed by agricultural activities (drainage, eutrophication, land use change), are indirect anthropogenic emissions. As a result, here, “natural and indirect anthropogenic sources” refer to “emissions that do not directly originate from fossil, agricultural, waste, and biomass burning sources” even if they are perturbed by anthropogenic activities and climate change. Natural and indirect anthropogenic emissions are split between “wetlands and inland freshwaters” and “other natural” emissions (e.g. wild animals, termites, land geological sources, oceanic geological and biogenic sources, and terrestrial permafrost). “Direct anthropogenic sources” are caused by direct human activities since pre-industrial/pre-agricultural times (3000–2000 BCE; Nakazawa et al., 1993) including agriculture, waste management, fossil-fuel-related activities, and biofuel and biomass burning (yet we acknowledge that a small fraction of wildfires are naturally ignited). Direct anthropogenic emissions are split between “agriculture and waste emissions”, “fossil fuel emissions”, and “biomass and biofuel burning emissions”, assuming that all types of fires are caused by anthropogenic activities. To conclude, this budget reports “direct anthropogenic” and “natural and indirect anthropogenic” methane emissions for the five main source categories explained above for both bottom-up and top-down approaches.

The sinks of methane are split into the soil uptake that can be derived from land surface models in the bottom-up budget and the chemical sinks. The chemical sinks are estimated by either chemistry climate or chemistry transport models in the bottom-up budget and are further detailed in terms of vertical distribution (troposphere and stratosphere) and oxidants.

Bottom-up estimates of CH₄ emissions for some processes are derived from process-oriented models (e.g. biogeochemical models for wetlands, models for termites), inventory models (agriculture and waste emissions, fossil fuel emissions, biomass and biofuel burning emissions), satellite-based models (large-scale biomass burning), or observation-
based upscaling models for other sources (e.g. inland water, geological sources). From these bottom-up approaches, it is possible to provide estimates for more detailed source sub-categories inside each main category described above (see budget in Table 3). However, the total CH₄ emission derived from the sum of independent bottom-up estimates remains unconstrained.

For atmospheric inversions (top-down approach), atmospheric methane concentration observations provide a constraint on the global methane total source if we assume the global sink is known (OH and other oxidant prescribed), or inversions also optimise the chemical sink. OH estimates are constrained by methyl chloroform inversion (Montzka et al., 2011; Rigby et al., 2017; Patra et al., 2021). The inversions reported in this work solve for the total net CH₄ flux at the surface (sum of sources minus soil uptake) (e.g. Pison et al., 2013) or a limited number of source categories (e.g. Bergamaschi et al., 2013). In most of the inverse systems the atmospheric oxidant concentrations were prescribed with pre-optimised or scaled OH fields, and thus the atmospheric sink is not optimised. The assimilation of CH₄ observations alone, as reported in this synthesis, can help to separate sources with different locations or temporal variations but cannot fully separate individual sources where they overlap in space and time in some regions. Top-down global and regional CH₄ emissions per source category were nevertheless obtained from gridded optimised fluxes, for the inversions that separated emissions into the five main GCP categories. Alternatively, for the inversion that only solved for total emissions (or for categories other than the five described above), the prior contribution of each source category at the spatial resolution of the inversion was scaled by the ratio of the total (or embedding category) optimised flux divided by the total (or embedding category) prior flux (Kirschke et al., 2013). In other words, the prior relative mix of sources at model resolution is kept in each grid cell while total emissions are given by the atmospheric inversions. The soil uptake was provided separately to report total gross surface emissions instead of net fluxes (sources minus soil uptake).

In summary, bottom-up models and inventory emissions are presented for all relevant source processes and grouped if needed into the five main categories defined above. Top-down inversion emissions are reported globally and for the five main emission categories.

## 2.5 Processing of emission maps and boxplot representation of emission budgets

Common data analysis procedures have been applied to the different bottom-up models, inventories, and atmospheric inversions whenever gridded products exist. Gridded emissions from atmospheric inversions and land surface models for wetland or biomass burning were provided at the monthly scale. Emissions from anthropogenic inventories are usually available as yearly estimates. These monthly or yearly fluxes

were provided on a 1° × 1° grid or regridded to 1° × 1° and then converted into units of Tg CH₄ per grid cell. Inversions with a resolution coarser than 1° were downscaled to 1° by each modelling group. Land fluxes in coastal pixels were reallocated to the neighbouring land pixel according to our 1° land-sea mask and vice versa for ocean fluxes. Annual and decadal means used for this study were computed from the monthly or yearly gridded 1° × 1° maps.

Budgets are presented as boxplots with quartiles (25%, median, 75%), outliers, and minimum and maximum values without outliers. Outliers were determined as values below the first quartile minus 3 times the interquartile range or values above the third quartile plus 3 times the interquartile range. Mean values reported in the tables are represented as “+” symbols in the corresponding figures.

# 3 Methane sources and sinks: bottom-up estimates

For each source category, a short description of the relevant processes, original data sets (measurements, models), and related methodology is given. More detailed information can be found in original publication references, in Annex A2 where the sources of data used to estimate the different sources and sinks are summarised and compared with those used in Saunois et al. (2020), and in the Supplement of this study when specified in the text. The emission estimates for each source category are compared with Saunois et al. (2020) in Table 3 and with Saunois et al. (2016) in Table S12 for the decade 2000–2009.

## 3.1 Direct anthropogenic sources

### 3.1.1 Global inventories

The main bottom-up global inventory data sets covering direct anthropogenic emissions from all sectors (Table 1) are from the United States Environmental Protection Agency (USEPA, 2019), the Greenhouse gas and Air pollutant Interactions and Synergies (GAINS) model developed by the International Institute for Applied Systems Analysis (IIASA) (Höglund-Isaksson et al., 2020), and the Emissions Database for Global Atmospheric Research (EDGARv6 and v7; Crippa et al., 2021, 2023) compiled by the European Commission Joint Research Centre (EC-JRC) and Netherlands Environmental Assessment Agency (PBL). We also used the Community Emissions Data System for historical emissions (CEDS) (Hoesly et al., 2018) developed for climate modelling and the Food and Agriculture Organisation (FAO) FAOSTAT emission database (Tubiello et al., 2022), which covers emissions from agriculture and land use (including peatland fires and biomass fires). These inventories are not independent as they may use the same activity data or emission factors, as discussed below.

These inventory data sets report emissions from fossil fuel production, transmission, and distribution; livestock enteric
<table><caption>**Table 1.** Bottom-up (BU) models and inventories for anthropogenic and biomass burning used in this study.</caption><thead><tr><th>BU models and inventories</th><th>Contribution</th><th>Time period (resolution)</th><th>Gridded</th><th>References</th></tr></thead><tbody><tr><td>CEDS (country-based)</td><td>fossil fuels, agriculture and waste, biofuel</td><td>1970–2019 (yearly)</td><td>no</td><td>Hoesly et al. (2018)</td></tr><tr><td>CEDS (gridded)<sup>a</sup></td><td>fossil fuels, agriculture and waste, biofuel</td><td>1970–2020 (monthly)</td><td>0.5 × 0.5°</td><td>Hoesly et al. (2018), O'Rourke et al. (2021)</td></tr><tr><td>EDGARv6</td><td>fossil fuels, agriculture and waste, biofuel</td><td>1990–2018<sup>b</sup> (yearly, monthly for some sectors)</td><td>0.1 × 0.1°</td><td>Oreggioni et al. (2021), Crippa et al. (2021)</td></tr><tr><td>EDGARv7</td><td>fossil fuels, agriculture and waste, biofuel</td><td>1990–2021 (yearly)</td><td>0.1 × 0.1°</td><td>Crippa et al. (2023)</td></tr><tr><td>IIASA GAINS v4.0</td><td>fossil fuels, agriculture and waste, biofuel</td><td>1990–2020 (yearly)</td><td>0.5 × 0.5°</td><td>Höglund-Isaksson et al. (2020)</td></tr><tr><td>USEPA</td><td>fossil fuels, agriculture and waste, biofuel, biomass burning</td><td>1990–2030 (10-year interval, interpolated to yearly)</td><td>no</td><td>USEPA (2019)</td></tr><tr><td>FAO-CH<sub>4</sub></td><td>agriculture, biomass burning</td><td>1961–2020<br>1990–2020 (yearly)</td><td>no</td><td>Federici et al. (2015), Tubiello et al. (2013), Tubiello (2019)</td></tr><tr><td>FINNv2.5</td><td>biomass burning</td><td>2002–2020 (daily)</td><td>1 km resolution</td><td>Wiedinmyer et al. (2023)</td></tr><tr><td>GFASv1.3</td><td>biomass burning</td><td>2003–2020 (daily)</td><td>0.1 × 0.1°</td><td>Kaiser et al. (2012)</td></tr><tr><td>GFEDv4.1s</td><td>biomass burning</td><td>1997–2020 (monthly)</td><td>0.25 × 0.25°</td><td>Giglio et al. (2013), van der Werf et al. (2017)</td></tr><tr><td>QFEDv2.5</td><td>biomass burning</td><td>2000–2020 (daily)</td><td>0.1 × 0.1°</td><td>Darmenov and da Silva (2015)</td></tr></tbody></table>

fermentation; manure management and application; rice cultivation; and solid waste and wastewater. Since the level of detail provided by country and by sector varies among inventories, the data were reconciled into common categories according to Table S2. For example, agricultural waste-burning emissions treated as a separate category in EDGAR, GAINS, and FAO are included in the biofuel sector in the USEPA inventory and in the agricultural sector in CEDS. The GAINS, EDGAR, and FAO estimates of agricultural waste burning were excluded from this analysis (these amounted to 1–3 Tg CH<sub>4</sub> yr<sup>-1</sup> in recent decades) to prevent any potential overlap with separate estimates of biomass burning emissions (e.g. GFEDv4.1s; Giglio et al., 2013; van der Werf et al., 2017). In the inventories used here, emissions for a given region/country and a given sector are usually calculated following IPCC methodology (IPCC, 2006), as the product of an activity factor and its associated emission factor.

An abatement coefficient may also be used, to account for any regulations implemented to control emissions (see e.g. Höglund-Isaksson et al., 2015). These data sets differ in their assumptions and data used for the calculation; however, they are not completely independent because they often use the same activity data, and some of them follow the same IPCC guidelines (IPCC, 2006). While the USEPA inventory adopts emissions reported by the countries to the UNFCCC, other inventories (FAOSTAT, EDGAR, and the GAINS model) produce their own estimates using a consistent approach for all countries, typically IPCC Tier 1 methods or deriving IPCC Tier 2 emission factors from country-specific information using a consistent methodology. These other inventories compile country-specific activity data and emission factor information or, if not available, adopt IPCC default factors (Tibrewal et al., 2024; Oreggioni et al., 2021; Höglund-Isaksson et al., 2020; Tubiello, 2019). CEDS takes

<footnote><sup>a</sup> Due to its limited sectoral breakdown, this data set is not used in Table 3. <sup>b</sup> Data set extends up to 2020, as stated in Sect. 2.2. and detailed in the Supplement (Sect. S4).</footnote>
a different approach (Hoesly et al., 2018) and combines data from GAINS, EDGAR, and FAO depending on the sector. Then their first estimates are scaled to match other individual or region-specific inventory values when available. This process maintains the spatial information in the default emission inventories while preserving consistency with country level data. The FAOSTAT data set (hereafter FAO-CH₄) provides estimates at the country level and is limited to agriculture (CH₄ emissions from enteric fermentation, manure management, rice cultivation, energy usage, burning of crop residues, and prescribed burning of savannahs) and land use (peatland fires and biomass burning). FAO-CH₄ uses activity data mainly from the FAOSTAT crop and livestock production database, as reported by countries to FAO (Tubiello et al., 2013), and applies mostly the Tier 1 IPCC methodology for emissions factors (IPCC, 2006), which depend on geographic location and development status of the country. For manure, the country-scale temperature was obtained from the FAO global agro-ecological zone database (GAEZv3.0, 2012). Although country emissions are reported annually to the UNFCCC by Annex-I countries, and episodically by non-Annex-I countries, data gaps of those national inventories do not allow the inclusion of these estimates in this analysis.

In this budget, we use the following versions of these inventories that were available at the start and during the analysis (see Table 1):

- EDGARv6 provides yearly gridded emissions by sector from 1970 to 2018 (Crippa et al., 2021; Oreggioni et al., 2021; EDGARv6 website https://edgar.jrc.ec.europa.eu/dataset_ghg60 (last access: 1 April 2025); Monforti Ferriario et al., 2021).

- EDGARv7 provides yearly gridded emissions by sector from 1970 to 2020 (monthly for some sectors), but emissions from fossil fuel energy are not separated (oil-gas and coal are lumped together – see Table S2) (EDGARv7 website: https://edgar.jrc.ec.europa.eu/dataset_ghg70 (last access: 1 April 2025); Crippa et al., 2023).

- GAINS model scenario version 4.0 (Höglund-Isaksson et al., 2020) provides an annual sectorial gridded product from 1990 to 2020 both by country and gridded. The USEPA (USEPA, 2019) provides 5-year sectorial totals by country from 1990 to 2020 (estimates from 2015 onward are a projection), with no gridded distribution available. The USEPA data set was linearly interpolated to provide yearly values from 1990–2020.

- CEDS version v_2021_04_21 provides gridded monthly and annual country-based emissions by sector from 1970 to 2019 (Hoesly et al., 2018; O'Rourke et al., 2021). Fossil fuel emissions for 2020 have been updated using the methodology described for CO in Zheng et al. (2023).

- FAO-CH₄ (database accessed in December 2022, FAO, 2022) contains annual country level data for the period 1961–2020, for rice, manure, and enteric fermentation and for 1990–2020 for burning savannah, crop residue, and non-agricultural biomass burning.

### 3.1.2 Total direct anthropogenic emissions

We calculated separately the total anthropogenic emissions for each inventory by adding its values for “agriculture and waste”, “fossil fuels”, and “biofuels” with additional large-scale biomass burning emissions data (Sect. 3.1.5). This method avoids double counting and ensures consistency within each inventory. This approach was used for the EDGARv6 and v7, CEDS, and GAINS inventories, but we kept the USEPA inventory as originally reported because it includes its own estimates of biomass burning emissions. FAO-CH₄ was only included in the range reported for the “agriculture and waste” category. For the latter, we calculated the range and mean value as the sum of the mean and range of the three anthropogenic subcategory estimates “enteric fermentation and manure”, “rice”, and “landfills and waste”. The values reported for the upper-level anthropogenic categories (“agriculture and waste”, “fossil fuels”, and “biomass burning and biofuels”) are therefore consistent with the sum of their subcategories, although there might be small percentage differences between the reported total anthropogenic emissions and the sum of the three upper-level categories. This approach provides a more accurate representation of the range of emission estimates, avoiding an artificial expansion of the uncertainty attributable to subtle differences in the definition of subsector categorisations between inventories.

Based on the ensemble of databases detailed above, total direct anthropogenic emissions were 358 [329–387] Tg CH₄ yr⁻¹ for the decade 2010–2019 (Table 3, including biomass and biofuel burning) and 331 [305–365] Tg CH₄ yr⁻¹ for the decade 2000–2009. Our estimate for the 2000–2009 decade is within the range of Saunois et al. (2020) (334 [321–358]), Saunois et al. (2016) (338 Tg CH₄ yr⁻¹ [329–342]), and Kirschke et al. (2013) (331 Tg CH₄ yr⁻¹ [304–368]) for the same period. The slightly larger range reported herein with respect to previous estimates is due to the USEPA lower estimate for agriculture, waste, and fossil emissions associated with the lowest estimate of biomass burning.

Figure 2 (left) summarises or projects global CH₄ emissions of anthropogenic sources (including biomass and biofuel burning) by different data sets between 2000 and 2050. The data sets consistently estimate total anthropogenic emissions of ~300 Tg CH₄ yr⁻¹ in 2000. For the Sixth Assessment Report of the IPCC, seven main Shared Socioeconomic Pathways (SSPs) were defined for future climate projections in the Coupled Model Intercomparison Project 6 (CMIP6) (Gidden et al., 2019; O'Neill et al., 2016) ranging from 1.9 to 8.5 W m⁻² radiative forcing by the year 2100 (as shown
<table><caption>**Table 2.** Biogeochemical models that computed wetland emissions used in this study. Model runs were performed with two climate inputs: CRU and GSWP3-W5E5. Models were run with prognostic (using their own calculation of wetland areas) and/or diagnostic (using WAD2M (Zhang et al., 2021b)) wetland surface areas (see Sect. 3.2.1).</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Institution</th><th colspan="2">Prognostic</th><th colspan="2">Diagnostic</th><th rowspan="2">References</th></tr><tr><th>CRU</th><th>GSWP3-W5E5</th><th>CRU</th><th>GSWP3-W5E5</th></tr></thead><tbody><tr><td>CH<sub>4</sub>MOD<sub>wetland</sub></td><td>Institute of Atmospheric Physics, CAS</td><td>n</td><td>n</td><td>y</td><td>y</td><td>Li et al. (2010)</td></tr><tr><td>CLASSIC</td><td>Environment and Climate Change Canada</td><td>y</td><td>y*</td><td>y</td><td>y*</td><td>Arora et al. (2018); Melton and Arora (2016)</td></tr><tr><td>DLEM</td><td>Boston College</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Tian et al. (2015, 2023)</td></tr><tr><td>ELM-ECA</td><td>Lawrence Berkeley National Laboratory</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Riley et al. (2011)</td></tr><tr><td>ISAM</td><td>University of Illinois, Urbana-Champaign</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Shu et al. (2020)<br>Xu et al. (2021)</td></tr><tr><td>JSBACH</td><td>MPI</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Kleinen et al. (2020, 2021, 2023)</td></tr><tr><td>JULES</td><td>UKMO</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Gedney et al. (2019)</td></tr><tr><td>LPJ-GUESS</td><td>Lund University</td><td>n</td><td>n</td><td>y</td><td>y</td><td>McGuire et al. (2012)</td></tr><tr><td>LPJ-MPI</td><td>MPI</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Kleinen et al. (2012)</td></tr><tr><td>LPJ-WSL</td><td>NASA GSFC</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Zhang et al. (2016b)</td></tr><tr><td>LPX-Bern</td><td>University of Bern</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Spahni et al. (2011), Stocker et al. (2014)</td></tr><tr><td>ORCHIDEE</td><td>LSCE</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Ringeval et al. (2011)</td></tr><tr><td>SDGVM</td><td>University of Birming-<br>ham/University of<br>Sheffield</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Beerling and<br>Woodward (2001),<br>Hopcroft et al. (2011,<br>2020)</td></tr><tr><td>TEM-MDM</td><td>Purdue University</td><td>n</td><td>n</td><td>y</td><td>y</td><td>Zhuang et al. (2004)</td></tr><tr><td>TRIPLEX-GHG</td><td>UQAM</td><td>n</td><td>n</td><td>y</td><td>y</td><td>Zhu et al. (2014, 2015)</td></tr><tr><td>VISIT</td><td>NIES</td><td>y</td><td>y</td><td>y</td><td>y</td><td>Ito and Inatomi (2012)</td></tr></tbody></table>

by the number in the SSP names). For the 1970–2015 period, historical emissions used in CMIP6 (Feng et al., 2019) combine anthropogenic emissions from CEDS (Hoesly et al., 2018) and a climatological value from the GFEDv4.1s biomass burning inventory (van Marle et al., 2017). The harmonised scenarios used for CMIP6 activities start in 2015 at 388 Tg CH₄ yr⁻¹, which corresponds to the higher range of our estimates. Since CH₄ emissions continue to track scenarios that assume no or minimal climate policies (SSP5 and SSP3), it may indicate that climate policies, when present, have not yet produced sufficient results to change the emissions trajectory substantially (Nisbet et al., 2019). After

2015, the SSPs span a range of possible outcomes, but current emissions appear likely to follow the higher-emission trajectories, given that over the past decade their trend has followed such trajectories and because the peak emission year has not yet been reached. High or medium emission reduction rates as suggested by scenarios SSP1 and SSP2 have not yet happened. This illustrates the challenge of methane mitigation that lies ahead to help reach the goals of the Paris Agreement (Nisbet et al., 2020; Shindell et al., 2024). In addition, estimates of methane atmospheric concentrations (Meinshausen et al., 2017, 2020) from the harmonised scenarios (Riahi et al., 2017) indicate that observations of global

<footnote>* CLASSIC uses GSWP3-W5E version 2 that covers the time period till 2016. All other models use GSWP-W5E5 version 3.</footnote>
<table><caption>**Table 3.** Global methane emissions by source type (in Tg CH₄ yr⁻¹) from Saunois et al. (2020) (left column pair) and from this work using bottom-up and top-down approaches. Because top-down models cannot fully separate individual processes, only five categories of emissions are provided (see text). Uncertainties are reported as [min–max] range of reported studies. The mean, minimum, and maximum values are calculated while discarding outliers, for each category of source and sink. As a result, discrepancies may occur when comparing the sum of categories and their corresponding total due to differences in outlier detections. Differences of 1 Tg CH₄ yr⁻¹ in the totals can also occur due to rounding errors. Compared to Saunois et al. (2020), emissions are split between “direct anthropogenic” emissions and “natural and indirect anthropogenic” sources. We also propose an estimate of the double counting between bottom-up wetland and inland freshwater ecosystem emissions. NA denotes “not available”.</caption><thead><tr><th colspan="3">Saunois et al. (2020)</th><th colspan="6">This work</th></tr><tr><th>Period of time</th><th colspan="2">2000–2009</th><th colspan="2">2000–2009</th><th colspan="2">2010–2019</th><th colspan="2">2020</th></tr><tr><th>Approaches</th><th>bottom-up</th><th>top-down</th><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 colspan="9">Natural and indirect anthropogenic sources</td></tr><tr><td>Combined wetlands and inland freshwaters</td><td>306 [229–391]</td><td>180 [153–196]</td><td>242 [156–355]</td><td>158 [145–172]</td><td>248 [159–369]</td><td>165 [145–214]</td><td>251 [171–364]</td><td>175 [151–229]</td></tr><tr><td>Wetlands</td><td>147 [102–179]</td><td>180 [153–196]</td><td>153 [116–189]<sup>a</sup></td><td>158 [145–172]</td><td>159 [119–203]<sup>a</sup></td><td>165 [145–214]</td><td>161 [131–198]<sup>a</sup></td><td>175 [151–229]</td></tr><tr><td>Inland freshwaters<sup>a</sup></td><td>159 [117–212]</td><td></td><td>112 [49–202]</td><td></td><td>112 [49–202]</td><td></td><td>112 [49–202]</td><td></td></tr><tr><td>Double counting<sup>b</sup></td><td>NA</td><td></td><td>–23 [–9 to –36]</td><td></td><td>–23 [–9 to –36]</td><td></td><td>–23 [–9 to –36]</td><td></td></tr><tr><td>Other natural sources</td><td>63 [26–94]</td><td>35 [21–47]</td><td>63 [24–93]</td><td>44 [40–46]</td><td>63 [24–93]</td><td>43 [40–46]</td><td>63 [24–93]</td><td>44 [40–47]</td></tr><tr><td>Land sources</td><td>50 [17–72]</td><td></td><td>51 [18–73]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Geological (onshore)</td><td>38 [13–53]</td><td></td><td>38 [13–53]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wild animals</td><td>2 [1–3]</td><td></td><td>2 [1–3]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Termites</td><td>9 [3–15]</td><td></td><td>10 [4–16]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Wildfires</td><td>**</td><td></td><td>**</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Permafrost soils (direct)</td><td>1 [0–1]</td><td></td><td>1 [0–1]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Vegetation</td><td>*</td><td></td><td>*</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Coastal and oceanic sources<sup>c</sup></td><td>13 [9–22]</td><td></td><td>12 [6–20]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Biogenic</td><td>6 [4–10]</td><td></td><td>5 [3–10]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Geological (offshore)</td><td>7 [5–12]</td><td></td><td>7 [5–12]</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total natural and indirect sources</td><td>369 [245–485]</td><td>215 [176–243]</td><td>305 [180–448]</td><td>204 [189–223]</td><td>311 [183–462]</td><td>206 [188–225]</td><td>314 [195–457]</td><td>216 [193–241]</td></tr><tr><td colspan="9">Direct anthropogenic sources</td></tr><tr><td>Agriculture and waste</td><td>192 [178–206]</td><td>202 [198–219]</td><td>194 [181–208]</td><td>210 [197–223]</td><td>211 [195–231]</td><td>228 [213–242]</td><td>211 [204–216]</td><td>245 [232–259]</td></tr><tr><td>Agriculture</td><td>132 [NA]</td><td></td><td>134 [125–142]</td><td></td><td>143 [132–155]</td><td></td><td>147 [143–149]</td><td></td></tr><tr><td>Livestock and manure</td><td>104 [93–109]</td><td></td><td>104 [100–110]</td><td></td><td>112 [107–118]</td><td></td><td>117 [114–124]</td><td></td></tr><tr><td>Rice cultivation</td><td>28 [23–34]</td><td></td><td>30 [24–34]</td><td></td><td>32 [25–37]</td><td></td><td>32 [29–37]</td><td></td></tr><tr><td>Landfills and waste</td><td>60 [55–63]</td><td></td><td>61 [52–71]</td><td></td><td>69 [56–80]</td><td></td><td>71 [60–84]</td><td></td></tr><tr><td>Fossil fuels</td><td>110 [94–129]</td><td>101 [71–151]</td><td>105 [97–123]<sup>b</sup></td><td>105 [88–115]</td><td>120 [117–125]<sup>b</sup></td><td>115 [100–124]</td><td>128 [120–133]<sup>b</sup></td><td>122 [101–133]</td></tr><tr><td>Coal mining</td><td>32 [24–42]</td><td></td><td>30 [26–32]</td><td></td><td>40 [37–44]</td><td></td><td>41 [38–43]</td><td></td></tr><tr><td>Oil and gas</td><td>73 [60–85]</td><td></td><td>65 [63–71]</td><td></td><td>67 [57–74]</td><td></td><td>74 [67–80]</td><td></td></tr><tr><td>Industry</td><td>2 [0–6]</td><td></td><td>4 [1–8]</td><td></td><td>5 [1–9]</td><td></td><td>5 [1–8]</td><td></td></tr><tr><td>Transport</td><td>4 [1–11]</td><td></td><td>3 [1–8]</td><td></td><td>2 [1–3]</td><td></td><td>2 [1–3]</td><td></td></tr><tr><td>Biomass and biofuel burning</td><td>31 [26–46]</td><td>29 [23–35]</td><td>30 [22–44]</td><td>26 [22–29]</td><td>28 [21–39]</td><td>27 [26–27]</td><td>27 [20–41]</td><td>26 [22–27]</td></tr><tr><td>Biomass burning</td><td>19 [15–32]</td><td></td><td>19 [14–29]</td><td></td><td>17 [12–24]</td><td></td><td>17 [13–27]</td><td></td></tr><tr><td>Biofuel burning</td><td>12 [9–14]</td><td></td><td>11 [8–14]</td><td></td><td>11 [8–14]</td><td></td><td>10 [7–14]</td><td></td></tr><tr><td>Total direct anthropogenic sources</td><td>334<sup>d</sup> [321–358]</td><td>332 [312–347]</td><td>333<sup>d</sup> [305–365]</td><td>341 [319–355]</td><td>358<sup>d</sup> [329–387]</td><td>369 [350–391]</td><td>372<sup>d</sup> [345–409]</td><td>392 [368–409]</td></tr><tr><td colspan="9">Sinks</td></tr><tr><td>Total chemical loss</td><td>595 [489–749]</td><td>505 [459–516]</td><td>585 [481–716]</td><td>504<sup>e</sup> [496–511]</td><td>602 [496–747]</td><td>521<sup>e</sup> [485–532]</td><td>602 [496–747]</td><td>538<sup>e</sup></td></tr><tr><td>Tropospheric OH</td><td>553 [476–677]</td><td></td><td>546 [446–663]</td><td></td><td>563 [462–663]</td><td></td><td>563 [462–663]</td><td>[503–554]</td></tr><tr><td>Stratospheric loss</td><td>31 [12–37]</td><td></td><td>37 [27–51]</td><td></td><td>37 [28–43]</td><td></td><td>37 [28–43]</td><td></td></tr><tr><td>Tropospheric Cl<sup>f</sup></td><td>11 [1–35]</td><td></td><td>6 [1–13]</td><td></td><td>6 [1–13]</td><td></td><td>6 [1–13]</td><td></td></tr><tr><td>Soil uptake</td><td>30 [11–49]</td><td>34 [27–41]</td><td>30 [11–49]</td><td>34 [34–34]</td><td>31 [11–49]</td><td>35 [35–35]</td><td>31 [11–49]</td><td>36 [35–36]</td></tr><tr><td>Total sinks</td><td>625 [500–798]</td><td>540 [486–556]</td><td>615 [492–765]</td><td>538 [530–545]<sup>c</sup></td><td>633 [507–796]</td><td>554 [520–567]<sup>c</sup></td><td>633 [507–796]</td><td>575 [566–589]<sup>c</sup></td></tr><tr><td colspan="9">Source–sink imbalance</td></tr><tr><td>Total sources</td><td>703 [566–842]</td><td>547 [524–560]</td><td>638 [485–813]</td><td>543 [526–558]</td><td>669 [512–849]</td><td>575 [553–586]</td><td>685 [540–865]</td><td>608 [581–627]</td></tr><tr><td>Total sinks</td><td>625 [500–798]</td><td>540 [486–556]</td><td>615 [492–765]</td><td>538 [530–545]<sup>c</sup></td><td>633 [507–796]</td><td>554 [550–567]<sup>c</sup></td><td>633 [507–796]</td><td>575 [566–589]<sup>c</sup></td></tr><tr><td>Imbalance</td><td>78</td><td>3 [–10 to 38]</td><td>23</td><td>5 [–4 to 13]<sup>e</sup></td><td>36</td><td>21 [19–33]<sup>e</sup></td><td>52</td><td>32 [15–38]<sup>e</sup></td></tr><tr><td>Atmospheric growth<sup>f</sup></td><td></td><td>5.8 [4.9–6.6]<sup>f</sup></td><td></td><td>6.1 [5.2–6.9]<sup>f</sup></td><td></td><td>20.9 [20.1–21.7]<sup>f</sup></td><td></td><td>41.8 [40.7–42.9]<sup>f</sup></td></tr></tbody></table>

<footnote>* Uncertain but likely small for upland forest and aerobic emissions, potentially large for forested wetland, but likely included elsewhere. ** We stop reporting this value to avoid potential double counting with satellite-based products of biomass burning (see Sect. 3.1.5). a Freshwater includes lakes, ponds, reservoirs, streams, and rivers; part of it is due to anthropogenic disturbances estimated in Sect. 3.2.2. b The double counting estimate is discussed in Sect. 3.2.2. c Flux from hydrates considered at 0 for this study is included, including estuaries. d Total anthropogenic emissions are based on estimates of full anthropogenic inventory and not on the sum of “agriculture and waste”, “fossil fuels” and “biofuel and biomass burning” categories (see Sect. 3.1.2). e Some inversions did not provide the chemical sink. These values are derived from a subset of the inversion ensemble. f Atmospheric growth rates are given in the same unit (Tg CH₄ yr⁻¹), based on the conversion factor of 2.75 Tg CH₄ ppb⁻¹ given by Prather et al. (2012) and the atmospheric growth rates provided in the text (in ppb yr⁻¹). g Here the numbers are from prognostic runs. To ensure a fair comparison with previous budgets (Saunois et al., 2020), the numbers are 163 [117–195] for 2000–2009 from diagnostic runs with CRU/CRU-JRA-55 climate inputs (see Sect. 3.2.1). h Up to 8 Tg of additional emissions could account for ultra-emitters (Lauvaux et al., 2022), as in Tibrewal et al. (2024), that are fully or partly missed in regular anthropogenic inventories.</footnote>
CH₄ concentrations fall well within the range of scenarios in absolute values, but their trend over the past few years is closest to those of scenario SSP5-8.5 (Fig. 2 right). The CH₄ concentrations are estimated using a simple exponential decay with inferred natural emissions (Meinshausen et al., 2011), and the emergence of any trend between observations and scenarios needs to be confirmed in the following years. However, the current observed concentrations and emissions estimates lie in the upper range of the former RCP scenarios starting in 2005 (Fig. S1). In the future, it will be important to monitor the trends from 2015 (the Paris Agreement) and from 2020 (Global Methane Pledge) estimated in inventories and from atmospheric observations and compare them to various scenarios.

### 3.1.3 Fossil fuel production and use

Most anthropogenic CH₄ emissions related to fossil fuels come from the exploitation, transportation, and usage of coal, oil, and natural gas. Additional emissions reported in this category include small industrial contributions such as the production of chemicals and metals, fossil fuel fires (e.g. underground coal mine fires and the Kuwait oil and gas fires), and transport (road and non-road transport). CH₄ emissions from the oil processing industry (e.g. refining) and production of charcoal are estimated to be a few teragrams of CH₄ per year only and are included in the transformation industry sector in the inventory. Fossil fuel fires are included in the subcategory “oil and gas”. Emissions from industry, road transport, and non-road transport are reported apart from the two main subcategories “oil and gas” and “coal”, as in Saunois et al. (2020) and contrary to Saunois et al. (2016); each of these amounts to about 2 to 5 Tg CH₄ yr⁻¹ (Table 3). The large range (1–9 Tg CH₄ yr⁻¹) is attributable to difficulties in allocating some sectors to these subsectors consistently among the different inventories (see Table S2). The spatial distribution of CH₄ emissions from fossil fuels is presented in Fig. 3 based on the mean gridded maps provided by CEDS, EDGARv6, and GAINS for the 2010–2019 decade; the USEPA lacks a gridded product.

Global mean emissions from fossil-fuel-related activities, other industries, and transport are estimated from the four global inventories (Table 1) to be 120 [117–125] Tg CH₄ yr⁻¹ for the 2010–2019 decade (Table 3), but with large differences in the rate of change during this period across inventories. The sector accounts on average for 34% (range 31%–42%) of total global anthropogenic emissions in 2010–2019. This contribution has slightly increased from 32% on average in 2000–2009.

#### Coal mining

During mining, CH₄ is emitted primarily from ventilation shafts, where large volumes of air are pumped in and out of the mine to keep the CH₄ mixing ratio below 0.5% to avoid

accidental ignition and from dewatering operations. In countries of the Organization for Economic Co-operation and Development (OECD), coalbed CH₄ is often extracted as fuel up to 10 years before the coal mine starts operation, thereby reducing the CH₄ channelled through ventilation shafts during mining. In many countries, large quantities of ventilation air CH₄ are still released to the atmosphere or flared, despite efforts to extend coal mine gas recovery under the UNFCCC Clean Development Mechanism (http://cdm.unfccc.int, last access: 1 April 2025). CH₄ leaks also occur during post-mining handling, processing, and transportation. Some CH₄ is released from coal waste piles and abandoned mines; while emissions from these sources were believed to be low (IPCC, 2000), recent work has estimated these at 22 × 10⁹ m³ (compared to 103 × 10⁹ m³ from functioning coal mines) in 2010 with emissions projected to increase into the future (Kholod et al., 2020).

In 2020, more than 35% (IEA, 2023a) of the world's electricity is still produced from coal. This contribution grew in the 2000s at the rate of several percent per year, driven by Asian economic growth where large reserves exist, but global coal consumption declined between 2014 and 2020. In 2020, the top 10 largest coal-producing nations accounted for ~90% of total world CH₄ emissions from coal mining; among them, the top three producers (China, the USA, and India) produced almost two-thirds (66%) of the world's coal (IEA, 2021).

Global estimates of CH₄ emissions from coal mining show a reduced range of 37–44 Tg CH₄ yr⁻¹ for 2010–2019 (Table 3), compared to the previous estimate for 2008–2017 in Saunois et al. (2020) reporting a range of 29–61 Tg CH₄ yr⁻¹ for 2008–2017. This reduced range probably results from using similar activity data (mostly from IEA statistics) in the different inventories. The highest value of the range in Saunois et al. (2020) came from the CEDS inventory, while the lowest came from the USEPA. CEDS seems to have revised downward their estimate compared to the previous version used in Saunois et al. (2020). There were previously large discrepancies in Chinese coal emissions, with a large overestimation from EDGARv4.2, on which CEDS was based. As highlighted by Liu et al. (2021a), a county-based inventory of Chinese methane emissions also confirms the overestimation of previous EDGAR inventories and estimated total anthropogenic Chinese emissions at 38.2 ± 5.5 Tg CH₄ yr⁻¹ for 2000–2008 (Liu et al., 2021a). Coal mining emission factors depend strongly on the type of coal extraction (underground mining emits up to 10 times more than surface mining), the geological underground structure (region-specific), history (basin uplift), and the quality of the coal (brown coal (lignite) emits less than hard coal (anthracite)). Finally, the different emission factors derived for coal mining are the main reason for the differences between inventories globally (Fig. 2).

For the 2010–2019 decade, methane emissions from coal mining represent 33% of total fossil-fuel-related emissions
<figure><img src="image_4.png" /><figcaption>**Figure 2.** (a) Global anthropogenic methane emissions (including biomass burning) over 2000–2050 from historical inventories (black line and shaded grey area) and future projections (coloured lines) (in Tg CH₄ yr⁻¹) from selected scenarios harmonised with historical emissions (CEDS) for CMIP6 activities (Gidden et al., 2019). Historical mean emissions correspond to the average of anthropogenic inventories listed in Table 1 added to the GFEDv4.1s (van der Werf et al., 2017) biomass burning historical emissions. (b) Global atmospheric methane concentrations for NOAA surface site observations (black) and projections based on SSPs (Riahi et al., 2017) with concentrations estimated using MAGICC (Meinshausen et al., 2017, 2020). Red dots show the last year available (2022 for observations).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** Methane emissions from four source categories: natural wetlands (excluding lakes, ponds, and rivers), biomass and biofuel burning, agriculture and waste, and fossil fuels for the 2010–2019 decade (in mg CH₄ m⁻² d⁻¹). The wetland emission map represents the mean daily emission average of the 16 biogeochemical models listed in Table 2 and over the 2010–2019 decade. Fossil fuel and agriculture and waste emission maps are derived from the mean estimates of gridded CEDS, EGDARv6, EDGARv7, and GAINS models. The biomass and biofuel burning map results from the mean of the biomass burning inventories listed in Table 1 added to the mean of the biofuel estimate from CEDS (O’Rourke et al., 2021), EDGARv6 (Crippa et al., 2021), EDGARv7 (Crippa et al., 2023), and GAINS (Höglund-Isaksson et al., 2020) models.</figcaption></figure>

of CH₄ (40 [37–44] Tg CH₄ yr⁻¹; Table 3). An additional assumed very small source corresponds to fossil fuel fires, which are mostly underground coal fires. This source is estimated at around 0.15 Tg yr⁻¹ in EDGARv7, though this value remains the same across EDGAR versions and for all years despite the changes in coal production, which could influence this estimate. However, to date, insufficient data are available to better estimate this largely unknown source.

## Oil and natural gas systems

This subcategory includes emissions from both conventional and shale oil and gas exploitation. Natural gas is composed primarily of CH₄, so both fugitive and planned emissions during the drilling of wells in gas fields, extraction, transportation, storage, gas distribution, end use, and incomplete combustion in gas flares emit CH₄ (Lamb et al., 2015; Shorter et al., 1996). Persistent fugitive emissions (e.g. due to
leaky valves and compressors) should be distinguished from intermittent emissions due to maintenance (e.g. purging and draining of pipes) or incidents. During transportation, fugitive emissions can occur in oil tankers, fuel trucks, and gas transmission pipelines, attributable to corrosion, manufacturing, and welding faults. According to Lelieveld et al. (2005), CH₄ fugitive emissions from gas pipelines should be relatively low; however, old distribution networks in some cities may have higher rates, especially those with cast-iron and unprotected steel pipelines (Phillips et al., 2013). Measurement campaigns in cities within the USA (e.g. McKain et al., 2015) and Europe (e.g. Defratyka et al., 2021) revealed that significant emissions occur in specific locations (e.g. storage facilities, city natural gas fuelling stations, well and pipeline pressurisation/depressurisation points, sewage systems, and furnaces of buildings) along the distribution networks (e.g. Jackson et al., 2014; McKain et al., 2015; Wunch et al., 2016). However, CH₄ emissions vary significantly from one city to another depending, in part, on the age of city infrastructure and the quality of its maintenance, making urban emissions difficult to scale up from measurement campaigns, although attempts have been made (e.g. Defratyka et al., 2021). In many facilities, such as gas and oil fields, refineries, and offshore platforms, most of the associated and other waste gas generated will be flared for security reasons with almost complete conversion to CO₂; however, due to the large quantities of waste gas generated, small fractions of gas still being vented make up relatively large quantities of methane. These two processes are usually considered together in inventories of oil and gas industries. In addition, single-point failure of natural gas infrastructure can leak CH₄ at high rate for months, such as at the Aliso Canyon blowout in Los Angeles, CA (Conley et al., 2016), or the shale gas well blowout in Ohio (Pandey et al., 2019), thus hampering emission control strategies. Production of natural gas from the exploitation of hitherto unproductive rock formations, especially shale, began in the 1970s in the USA on an experimental or small-scale basis, and then, from the early 2000s, exploitation started at a large commercial scale. The shale gas contribution to total dry natural gas production in the USA reached 82% in 2023, growing rapidly from 48% in 2013 (IEA, 2023b). The possibly larger emission factors from shale gas compared to conventional gas have been widely debated (e.g. Cathles et al., 2012; Howarth, 2019; Lewan, 2020). The latest studies tend to infer emission factors from the oil-gas production chain of about 1% to 6% (e.g. Schneising et al., 2020; Varon et al., 2023; Zhang et al., 2020), but the loss rate could be as high as more than 10% in low-producing well sites (e.g. Omara et al., 2022, Williams et al., 2025).

CH₄ emissions from oil and natural gas systems vary greatly in different global inventories (67 to 80 Tg yr⁻¹ in 2020; Table 3). The inventories generally rely on the same sources and magnitudes for activity data, with the derived differences therefore resulting primarily from different

methodologies and parameters used, including emission factors. Those factors are country- or even site-specific, and the few field measurements available often combine oil and gas activities (Brandt et al., 2014), resulting in high uncertainty in emission estimates for many major oil- and gas-producing countries. Depending on the region, the IPCC 2006 default emission factors may vary by 2 orders of magnitude for oil production and 1 order for gas production. For instance, the GAINSv4.0 estimate of CH₄ emissions from US oil and gas systems in 2015 is 16 Tg, which is almost twice as high as EDGARv8.0 (EDGAR, 2023) at 8.4 Tg and the USEPA (USEPA, 2019) at 9.5 Tg. The difference can partly be explained by GAINS using a bottom-up methodology to derive country- and year-specific flows of associated petroleum gas and attributing these to recovery/reinjection, to flaring or venting (Höglund-Isaksson, 2017), and partly to GAINS using a higher emission factor for unconventional gas production (Höglund-Isaksson et al., 2020). Recent quantifications using satellite observations and inversion estimate a relatively stable trend for US oil and gas system emissions since 2010, with Lu et al. (2023) estimating 14.6 Tg for 2010, 15.9 Tg for 2014, and 15.6 Tg for 2019; Shen et al. (2022) estimating a mean of 12.6 Tg for 2018–2020; and Maasakkers et al. (2021) a mean of 11.1 Tg for 2010 to 2015. The stable top-down trend for the USA appears not well captured in the bottom-up inventories from GAINS and EDGAR, which tend to show an increasing trend driven by an increase in production volumes.

Most recent studies (e.g. Zhang et al., 2020; Shen et al., 2023; Li et al., 2024, Tibrewal et al., 2024; Sherwin et al., 2024) still suggest that the methane emissions from oil and gas industry are underestimated by inventories, industries, and agencies, including the USEPA and UNFCCC reporting. Lauvaux et al. (2022) showed that emissions from a few high-emitting facilities, i.e. super-emitters (>$20 t h⁻¹$), which are usually sporadic in nature and not accounted for in the inventories, could represent 8%–12% of global oil and gas emissions, or around 8 Tg CH₄ yr⁻¹. These high-emitting points, located on the conventional part of the facilities, could be avoided through better operating conditions and repair of malfunctions. Over the last decade, absolute CH₄ emissions have almost certainly increased, since US crude oil production doubled and natural gas production rose by about 50% (IEA, 2023a). However, global implications of the rapidly growing shale gas activity in the USA remain to be determined precisely.

For the 2010–2019 decade, CH₄ emissions from upstream and downstream oil and natural gas sectors are estimated to represent about 56% of total fossil CH₄ emissions (67 [57–74] Tg CH₄ yr⁻¹; Table 3) based on global inventories, with a lower uncertainty range than for coal emissions for most countries. However, it is worth noting that 8 Tg CH₄ yr⁻¹ should be added on top of this estimate to acknowledge the ultra-emitter contribution, as done in Tibrewal et al. (2024).
### 3.1.4 Agriculture and waste sectors

This main category includes CH₄ emissions related to livestock production (i.e. enteric fermentation in ruminant animals and manure management), rice cultivation, landfills, and wastewater handling. Of these activities, globally and in most countries, livestock is by far the largest source of CH₄, followed by waste handling and rice cultivation. Conversely, field burning of agricultural residues is a minor source of CH₄ reported in emission inventories (a few teragrams at the global scale). The spatial distribution of CH₄ emissions from agriculture and waste handling is presented in Fig. 3 based on the mean gridded maps provided by CEDS, EDGARv6, and GAINS over the 2010–2019 decade.

Global emissions from agriculture and waste for the period 2010–2019 are estimated to be 211 [195–231] Tg CH₄ yr⁻¹ (Table 3), representing 60% of total direct anthropogenic emissions. Agriculture emissions amount to 144 Tg CH₄ yr⁻¹, 40% of the direct anthropogenic emissions, with the rest coming from the fossil fuel sector (34%), waste (19%), and biomass (5%) and biofuel (3%) burning.

#### Livestock: fermentation and manure management

Enteric domestic ruminants such as cattle, buffalo, sheep, goats, and camels emit CH₄ as a by-product of the anaerobic microbial activity in their digestive systems (Johnson et al., 2002). The very stable temperatures (about 39 °C) and pH (6.5–6.8) within the rumen of domestic ruminants, along with a constant plant matter flow from grazing (cattle graze many hours per day), allow methanogenic Archaea residing within the rumen to produce CH₄. CH₄ is released from the rumen mainly through the mouth of multi-stomached ruminants (eructation, ~90% of emissions) or absorbed in the blood system. The CH₄ produced in the intestines and partially transmitted through the rectum is only ~10% (Hill et al., 2016).

The total number of livestock continues to grow steadily. There are currently (2020) about 1.5 billion cattle globally, almost 1.3 billion sheep, and nearly as many goats (http://www.fao.org/faostat/en/#data/GE, last access: 1 April 2025). Livestock numbers are linearly related to CH₄ emissions in inventories using the Tier 1 IPCC approach such as FAOSTAT. In practice, some non-linearity may arise due to dependencies of emissions on the total weight of the animals and their diet, which are better captured by Tier 2 and higher approaches. Cattle, due to their large population, large individual size, and particular digestive characteristics, account for the majority of enteric fermentation CH₄ emissions from livestock worldwide (Tubiello, 2019; FAO, 2022), particularly in intensive agricultural systems in wealthier and emerging economies, including the USA (USEPA, 2016). CH₄ emissions from enteric fermentation also vary from one country to another as cattle may experience diverse living

conditions that vary spatially and temporally, especially in the tropics (Chang et al., 2019).

Anaerobic conditions often characterise manure decomposition in a variety of manure management systems globally (e.g. liquid/slurry treated in lagoons, ponds, tanks, or pits), with the volatile solids in manure producing CH₄. In contrast, when manure is handled as a solid (e.g. in stacks or dry lots) or deposited on pasture, range, or paddock lands, it tends to decompose aerobically and to produce little or no CH₄. However aerobic decomposition of manure tends to produce nitrous oxide (N₂O), which has a larger global warming impact than CH₄. Ambient temperature, moisture, energy contents of the feed, manure composition, and manure storage or residency time affect the amount of CH₄ produced. Despite these complexities, most global data sets used herein apply a simplified IPCC Tier 1 approach, where amounts of manure treated depend on animal numbers and simplified climatic conditions by country.

Global CH₄ emissions from enteric fermentation and manure management are estimated in the range of 114–124 Tg CH₄ yr⁻¹, for the year 2020, in the GAINS model and CEDS, USEPA, FAO-CH₄, and EDGARv7 inventories (Table 3). Using the Tier 2 method adopted from the 2019 refinement to 2006 IPCC guidelines, a recent study (Zhang et al., 2022) estimated that global CH₄ emissions from livestock increased from 31.8 [26.5–37.1] (mean [minimum–maximum] of 95% confidence interval) Tg CH₄ yr⁻¹ in 1890 to 131.7 [109.6–153.7] Tg CH₄ yr⁻¹ in 2019, a 4-fold increase in the past 130 years. Chang et al. (2021) estimate enteric fermentation and manure management emissions based on mixed Tier 1–2 and Tier 1 approaches and calculate livestock emissions of 120 ± 13 and 136 ± 15 Tg CH₄ yr⁻¹ respectively for 2018. Chang et al. (2021) and Zhang et al. (2022) estimates for 2018 and 2019 are on average a bit higher than the inventories estimates but in agreement considering the uncertainties. It is worth recalling here that the ranges provided in this study correspond to the minimum–maximum of the existing estimates and do not include the uncertainty of the individual estimate; these uncertainties could be larger than the range proposed here.

For the period 2010–2019, we estimated total emissions of 112 [107–118] Tg CH₄ yr⁻¹ for enteric fermentation and manure management, about one-third of total global anthropogenic emissions (Table 3).

#### Rice cultivation

Most of the world's rice is grown in flooded paddy fields (Elphick, 2010). The water management systems, particularly flooding, used to cultivate rice are one of the most important factors influencing CH₄ emissions and one of the most promising approaches for CH₄ emission mitigation: periodic drainage and aeration not only cause existing soil CH₄ to oxidise, but also inhibit further CH₄ production in soils (Simp-
son et al., 1995; USEPA, 2016; Zhang et al., 2016a). Upland rice fields are not typically flooded and therefore are not a significant source of CH₄. Other factors that influence CH₄ emissions from flooded rice fields include fertilisation practices (i.e. the use of urea and organic fertilisers), soil temperature, soil type (texture and aggregated size), and rice variety and cultivation practices (e.g. tillage, seeding, and weeding practices) (Conrad et al., 2000; Kai et al., 2011; USEPA, 2012; Yan et al., 2009). For instance, CH₄ emissions from rice paddies increase with organic amendments (Cai et al., 1997) but can be mitigated by applying other types of fertilisers (mineral, composts, biogas residues) or using wet seeding (Wassmann et al., 2000).

The geographical distribution of rice emissions has been assessed by global (e.g. Janssens-Maenhout et al., 2019; Tubiello, 2019; USEPA, 2012) and regional (e.g. Castelán-Ortega et al., 2014; Chen et al., 2013; Chen and Prinn, 2006; Peng et al., 2016; Yan et al., 2009; Zhang and Chen, 2014) inventories and land surface models (Li et al., 2005; Pathak et al., 2005; Ren et al., 2011; Spahni et al., 2011; Tian et al., 2010, 2011; Zhang et al., 2016a). The emissions show a seasonal cycle, peaking in the summer months in the extratropics, associated with monsoons and land management. Emissions from rice paddies are influenced not only by the extent of rice field area, but also by changes in the productivity of plants (Jiang et al., 2017) as these alter the CH₄ emission factor used in inventories. However, the inventories considered herein are largely based on IPCC Tier 1 methods, which mainly scale with cultivated areas and include region-specific emission factors but do not account for changes in plant productivity and detailed cultivation practices.

The largest emissions from rice cultivation are found in Asia, accounting for 30% to 50% of global emissions (Fig. 3). The decrease in CH₄ emissions from rice cultivation over recent decades is confirmed in most inventories because of the decrease in rice cultivation area, changes in agricultural practices, and a northward shift of rice cultivation since the 1970s, as in China (e.g. Chen et al., 2013).

Based on the global inventories considered in this study, global CH₄ emissions from rice paddies are estimated to be 32 [25–37] Tg CH₄ yr⁻¹ for the 2010–2019 decade (Table 3), or about 9% of total global anthropogenic emissions of CH₄. These estimates are consistent with the 29 Tg CH₄ yr⁻¹ estimated for the year 2000 by Carlson et al. (2017).

## Waste management

This sector includes emissions from managed and non-managed landfills (solid waste disposal on land) and wastewater handling, where all kinds of waste are deposited. CH₄ production from waste depends on the pH, moisture, and temperature of the material. The optimum pH for CH₄ emission is between 6.8 and 7.4 (Thorneloe et al., 2000). The development of carboxylic acids leads to low pH, which limits methane emissions. Food or organic waste, such as leaves

and grass clippings, ferment quite easily, while wood and wood products generally ferment slowly and cellulose and lignin even more slowly (USEPA, 2010a).

Waste management was responsible for about 11% of total global direct anthropogenic CH₄ emissions in 2000 (Kirschke et al., 2013). A recent assessment of CH₄ emissions in the USA found landfills to account for almost 26% of total US anthropogenic CH₄ emissions in 2014, the largest contribution of any single CH₄ source in the USA (USEPA, 2016). In Europe, gas control has been mandatory on all landfills since 2009, and more importantly for CH₄ emissions, the EU Landfill Directive (1999) with subsequent amendments, has diverted most biodegradable waste away from landfills towards source separation, recycling, composting, and energy recovery, with a legally binding target not to landfill more than 10% of municipal solid waste by 2035.

Wastewater from domestic and industrial sources is treated in municipal sewage treatment facilities and private effluent treatment plants. The principal factor in determining the CH₄ generation potential of wastewater is the amount of degradable organic material in the wastewater. Wastewater with high organic content is treated anaerobically, which leads to increased emissions (André et al., 2014). Excessive and rapid urban development worldwide, especially in Asia and Africa, could enhance methane emissions from waste unless adequate mitigation policies are designed and implemented rapidly.

The GAINS model and CEDS and EDGAR inventories give robust emission estimates from solid waste in the range of 37–42 Tg CH₄ yr⁻¹ for the year 2019 and more uncertain wastewater emissions in the range 20–45 Tg CH₄ yr⁻¹.

In our study, the global emission of CH₄ from waste management is estimated in the range of 56–80 Tg CH₄ yr⁻¹ for the 2010–2019 period with a mean value of 69 Tg CH₄ yr⁻¹, about 19% of total global anthropogenic emissions (Table 3).

### 3.1.5 Biomass and biofuel burning

This category includes CH₄ emissions from biomass burning in forests, savannahs, grasslands, peats, and agricultural residues, as well as from the burning of biofuels in the residential sector (stoves, boilers, fireplaces). Biomass and biofuel burning emit CH₄ under incomplete combustion conditions (i.e. when oxygen availability is insufficient for complete combustion), for example in charcoal manufacturing and smouldering fires. The amount of CH₄ emitted during the burning of biomass depends primarily on the amount of biomass, burning conditions, fuel moisture, and the specific material burned.

In this study, we use large-scale biomass burning (forest, savannah, grassland, and peat fires) from five biomass burning inventories (described below) and the biofuel burning contribution from anthropogenic emission inventories (EDGARv6 and v7, CEDS, GAINS, and USEPA). The spatial distribution of emissions from the burning of biomass
and biofuel over the 2010–2019 decade is presented in Fig. 3 based on data listed in Table 1.

At the global scale, during the period of 2010–2019, biomass and biofuel burning generated CH₄ emissions of 28 [21–39] Tg CH₄ yr⁻¹ (Table 3), of which 30%–50% is from biofuel burning.

## Biomass burning

Fire is an important disturbance event in terrestrial ecosystems globally (van der Werf et al., 2010) and can be of either natural (typically ~10% of fires, ignited by lightning strikes or started accidentally) or anthropogenic origin (~90%, human-initiated fires) (USEPA, 2010b, Chap. 9.1). As previously noted all fires are counted as anthropogenic in Table 3. Anthropogenic fires are concentrated in the tropics and subtropics, where forests, savannahs, and grasslands may be burned to clear land for agricultural purposes or to maintain pastures and rangelands. Small fires associated with agricultural activity, such as field burning and agricultural waste burning, are often not detected by moderate-resolution remote sensing methods and are instead estimated based on cultivated area or through in situ measurements such as dedicated airborne campaigns (e.g. Barker et al., 2020).

Emission rates of biomass burning vary with biomass loading (depending on the biomes) at the location of the fire, the efficiency of the fire (depending on the vegetation type), the fire type (smouldering or flaming), and emission factor (mass of the considered species/mass of biomass burned). Depending on the approach, these parameters can be derived using satellite data and/or biogeochemical model or through simpler IPCC default approaches.

In this study, we use five products to estimate biomass burning emissions. The Global Fire Emission Database (GFED) is the most widely used global biomass burning emission data set and provides estimates from 1997 onwards. Here, we use GFEDv4.1s (van der Werf et al., 2017), based on the Carnegie–Ames–Stanford approach (CASA) biogeochemical model (van der Werf et al., 2010) driven by satellite-derived vegetation characteristics and burned area mostly from the MODerate resolution Imaging Sensor, MODIS (Giglio et al., 2013). GFEDv4.1s (with small fires) is available at a 0.25° resolution and on a daily basis from 1997 to 2020. One characteristic of the GFEDv4.1s burned area is that small fires are better accounted for compared to GFEDv4.1 (Randerson et al., 2012), increasing carbon emissions by approximately 35% at the global scale. The latest version GFEDv5 (Chen et al., 2023) suggests a 61% higher burned area than GFEDv4.1s, in closer agreement with burned area products from higher-resolution satellite sensors. The next budget would benefit from GFEDv5 to revisit the estimates of biomass burning emissions (which would likely go up) based on more specific comparison studies.

The Quick Fire Emissions Dataset (QFED) is calculated using the fire radiative power (FRP) approach, in which the thermal energy emitted by active fires (detected by MODIS) is converted to an estimate of CH₄ flux using biome-specific emissions factors and a unique method of accounting for cloud cover. Further information related to this method and the derivation of the biome-specific emission factors can be found in Darmenov and da Silva (2015). Here we use the historical QFEDv2.5 product available daily on a 0.1 × 0.1 grid for 2000 to 2020.

The Fire INventory from the National Center for Atmospheric Research (FINNv2.5; Wiedinmyer et al., 2023) provides daily, 1 km resolution estimates of gas and particle emissions from open burning of biomass (including wildfire, agricultural fires, and prescribed burning) over the globe for the period 2002–2020. FINNv2.5 uses MODIS and VIIRS satellite observations for active fires, land cover, and vegetation density.

We use v1.3 of the Global Fire Assimilation System (GFAS; Kaiser et al., 2012), which calculates emissions of biomass burning by assimilating fire radiative power (FRP) observations from MODIS at a daily frequency and 0.5° resolution and is available for 2000–2020.

The FAO-CH₄ yearly biomass burning emissions are based on the most recent MODIS 6 burned area products (Prosperi et al., 2020), coupled with a pixel level (500 m) implementation of the IPCC Tier 1 approach, and are available from 1990 to 2020 (Table 1).

The differences in emission estimates for biomass burning arise from specific geographical and meteorological conditions and fuel composition, which strongly impact combustion completeness and emission factors. The latter vary greatly according to fire type, ranging from 2.2 g CH₄ kg⁻¹ dry matter burned for savannah and grassland fires up to 21 g CH₄ kg⁻¹ dry matter burned for peat fires (van der Werf et al., 2010). Biomass burning emissions encountered large interannual variability related to meteorological conditions, with generally higher emissions during El Niño periods as in 2019 (20 [14–28] Tg CH₄ yr⁻¹), 2015 (22 [15–28] Tg CH₄ yr⁻¹), and 2010 to a lesser extent (18 [15–29] Tg CH₄ yr⁻¹).

In this study, based on the five aforementioned products, biomass burning emissions are estimated at 17 Tg CH₄ yr⁻¹ [12–24] for 2010–2019, representing about 5% of total global anthropogenic CH₄ emissions (Table 3).

## Biofuel burning

Burning of biomass to produce energy for domestic, industrial, commercial, or transportation purposes is hereafter called biofuel burning. A largely dominant fraction of CH₄ emissions from biofuel burning comes from domestic cooking or heating in stoves, boilers, and fireplaces, mostly in open cooking fires where wood, charcoal, agricultural residues, or animal dung are burned. It is estimated that
more than 2 billion people, mostly in developing countries, use solid biofuels to cook and heat their homes daily (André et al., 2014), yet CH₄ emissions from biofuel combustion have received relatively little attention. Biofuel burning estimates are gathered from the CEDS, USEPA, GAINS, and EDGAR inventories. Due to the sectoral breakdown of the EDGAR and CEDS inventories, the biofuel component of the budget has been estimated as equivalent to the “RCO – Energy for buildings” sector as defined in Worden et al. (2017) and Hoesly et al. (2018) (Table S2). This is equivalent to the sum of the IPCC 1.A.4.a - Commercial/Institutional, 1.A.4.b - Residential, 1.A.4.c - Agriculture/Forestry/Fishing/Fish farms, and 1.A.5 - Non-Specified reporting categories. This definition is consistent with that used in Saunois et al. (2016) and Kirschke et al. (2013). While this sector incorporates biofuel use, it also includes the use of other combustible materials (e.g. coal or gas) for small-scale heat and electricity generation within residential and commercial premises. Data provided by the GAINS inventory suggest that this approach may overestimate biofuel emissions by between 5% and 50%. Further study into this category would be needed to better disentangle biofuels from fossil combustibles.

In our study, biofuel burning is estimated to contribute 11 [8–14] Tg CH₄ yr⁻¹ to the global CH₄ budget, about 3% of total global anthropogenic CH₄ emissions for 2010–2019 (Table 3).

### 3.1.6 Other anthropogenic sources (not explicitly included in this study)

Other anthropogenic sources not included in this study are related to agriculture and land use management. In particular, increases in agricultural areas (such as global palm oil production) have led to the clearing of natural peat forests, reducing natural peatland area and associated natural CH₄ emissions. Peatlands planted to forests (like in northern Europe) also lead to reduced CH₄ emissions. While studies have long suggested that CH₄ emissions from peatland drainage ditches are likely to be significant (e.g. Minkkinen and Laine, 2006; Peacock et al., 2021), CH₄ emissions related to palm oil plantations have yet to be properly quantified (e.g. Manning et al., 2019). Taylor et al. (2014) have quantified global palm oil wastewater treatment fluxes to be 4 ± 32 Tg CH₄ yr⁻¹ for 2010–2013. This currently represents a small and highly uncertain source of methane but one potentially growing in the future.

## 3.2 Natural and indirect anthropogenic sources

As introduced in Sect. 2.4, natural and indirect anthropogenic sources refer to pre-agricultural CH₄ emissions even if they are perturbed by anthropogenic climate change or other global change factors (e.g. eutrophication) and indirect emissions resulting from anthropogenic perturbation of the

landscape (reservoirs) and the biogeochemical characteristics of soil. They include vegetated wetland emissions and inland freshwater systems (lakes, small ponds, reservoirs, and rivers), land geological sources (gas–oil seeps, mud volca noes, microseepage, geothermal manifestations, and volca noes), wild animals, wildfires, termites, thawing terrestrial and marine permafrost, and coastal and oceanic sources (bi ogenic, geological, and hydrate). In water-saturated or flooded ecosystems, the decomposition of organic matter gradually depletes most of the oxygen in the soil or the sediment zone, resulting in anaerobic conditions and CH₄ production. Once produced, CH₄ can reach the atmosphere through a combi nation of three processes: (1) diffusive loss of dissolved CH₄ across the air–water boundary, (2) ebullition flux from sed iments, and (3) flux mediated by emergent aquatic macro phytes and terrestrial plants (plant transport). On its way to the atmosphere, in the soil or water columns, CH₄ can be partly or completely oxidised by microorganisms, which use CH₄ as a source of energy and carbon (USEPA, 2010b). Con currently, methane from the atmosphere can diffuse into the soil column and be oxidised (see Sect. 3.3.4 on soil uptake).

### 3.2.1 Wetlands

Wetlands are generally defined as ecosystems in which mineral or peat soils are water-saturated at some depth or where surface inundation (permanent or not) has a dominating influence on the soil biogeochemistry and determines the ecosystem species composition (USEPA, 2010b). To refine such an overly broad definition for CH₄ emissions, we define wetlands as ecosystems with inundated or saturated soils or peats where anaerobic conditions below the water table lead to CH₄ production (Matthews and Fung, 1987; USEPA, 2010b). Brackish water emissions are discussed separately in Sect. 3.2.6. Our definition of wetlands includes ombrotrophic and minerotrophic peatlands (i.e. bogs and fens), mineral soil wetlands (swamps and marshes), and seasonal or permanent floodplains. It excludes exposed water surfaces without emergent macrophytes, such as lakes, rivers, estuaries, ponds, and reservoirs (addressed in the next section), as well as rice agriculture (see Sect. 3.1.4, rice cultivation paragraph) and wastewater ponds. It also excludes coastal vegetated ecosystems (mangroves, seagrasses, salt marshes) with salinities usually > 0.5 (see Sect. 3.2.6). Even with this definition, some wetlands could be considered anthropogenic systems, being affected by human land use changes such as impoundment, drainage, or restoration (Woodward et al., 2012). In the following, we retain the generic denomination “wetlands” for natural and human-influenced wetlands, as discussed in Sect. 2.2.

The three most important factors influencing CH₄ production in wetlands are the spatial and temporal extent of anoxia (linked to water saturation), temperature, and substrate availability (Valentine et al., 1994; Wania et al., 2010; Whalen, 2005; Delwiche et al., 2021; Knox et al., 2021).
Land surface models estimate CH₄ emissions through a series of processes, including CH₄ production, oxidation, and transport. The models are then forced with inputs accounting for changing environmental factors (Melton et al., 2013; Poulter et al., 2017; Tian et al., 2010; Wania et al., 2013; Xu et al., 2010). CH₄ emissions from wetlands are computed as the product of an emission flux density and a CH₄-producing area or surface extent (Bohn et al., 2015; Melton et al., 2013). The areal extent of different wetland types (having large differences in areal CH₄ emission rates) appears to be a primary contributor to uncertainties in the absolute flux of CH₄ emissions from wetlands, with meteorological response being the main source of uncertainty for seasonal and interannual variability (Poulter et al., 2017; Kuhn et al., 2021; Parker et al., 2022; McNicol et al., 2023; Karlson and Bastviken, 2023). However, large uncertainty remains in both spatial and temporal emission distributions, especially over tropical wetlands where data are lacking to evaluate the models but are nevertheless a key region for climate feedbacks (Nisbet, 2023; Zhang et al., 2023). Direct measurement campaigns and remote sensing are providing key insights into where to improve the land surface models (e.g. France et al., 2022; Shaw et al., 2022).

In this work, 16 land surface models computing net CH₄ emissions (Table 2) were run under a common protocol with a spin-up using repeated climate data from 1901–1920 to pre-industrial conditions followed by a transient simulation through the end of 2020. Of the 16 models, 13 previously contributed to Saunois et al. (2020), and 3 models were new to this release (CH₄MOD<sub>wetland</sub> (Li et al., 2010), ISAM (Shu et al., 2020; Xu et al., 2021), and SDGVM (Beerling and Woodward, 2001; Hopcroft et al., 2011; Hopcroft et al., 2020)) (Table 2; see also in the Supplement Table S3 for a history of the contributing models). Climatic forcing uncertainties are considered in the ensemble estimate by using two climate data sets: CRU/CRU-JRA55 (Harris et al., 2014) and GSWP3-W5E5 (Dirmeyer et al., 2006; Kim, 2017; Lange, 2019; Cucchi et al., 2020). Atmospheric CO₂ was also prescribed in the models. For all models, two wetland area dynamic schemes were applied: a diagnostic scheme using a remote-sensing-based wetland area and dynamics data set called WAD2M (Wetland Area Dynamics for Methane Modeling; Zhang et al., 2021a, b) available at 0.25° of horizontal resolution, as in Saunois et al. (2020), and a prognostic scheme using internal model-specific hydrologic models.

The diagnostic wetland extent product WAD2Mv1.0 (Zhang et al., 2021a) has been updated since the work of Saunois et al. (2020) to WAD2Mv2.0 (Zhang et al., 2021b) and extended to 2020. It uses the same Surface Water Microwave Product Series (SWAMPSv3.2) for capturing inundation dynamics (Jensen and McDonald, 2019), which was extended to 2020. To reduce potential double counting with the freshwater budget, the surface areas of rivers/streams and lakes/ponds are excluded by using the products Global River Widths from Landsat (GRWL) database v01.01 (Allen and

Pavelsky, 2018) and HydroLAKES v1.0 (Messenger et al., 2006), instead of the Global Surface Water (GSW) product (Pekel et al., 2016) used in WAD2Mv1.0. The GRWL and HydroLAKES are also the data sets used separately in the upscaling of the freshwater budget, allowing for a more consistent approach between the wetland and freshwater CH₄ budgets (Sect. 3.2.2). This update in WAD2M leads to a downward revised annual average wetland extent by 0.5 Mkm² for the mid-high latitudes (mainly due to larger lake extent in HydroLAKES than in the GSW data set) with small impacts in other regions. However, since HydroLAKES includes only vectorised lakes larger than 0.1 km², smaller lakes/ponds under 0.1 km² are implicitly still included as wetlands in WAD2Mv2.0. For the high-latitude region, the recent peatland extent product from Hugelius et al. (2020) is applied, which indicates a slightly higher peatland area by 0.2 Mkm², primarily in regions above 60°N, compared to the Northern Circumpolar Soil Carbon Database (NCSCD) product (Hugelius et al., 2013) used in WAD2Mv1.0. Rice agriculture was removed using the Monthly Irrigated and Rainfed Crop Areas (MIRCA2000; Portmann et al., 2010) data set from circa 2000, as a fixed distribution.

The combined remote-sensing and inventory WAD2Mv2.0 product leads to a maximum wetland area of 13.6 Mkm² during the peak season (7.9 Mkm² on annual average, with a range of 7.5 to 8.4 Mkm² from 2000–2020, about 5.2% of the global land surface). The largest wetland areas in WAD2Mv2.0 are in Amazonia, the Congo Basin, and the Western Siberian lowlands, which in previous studies were underestimated by inventories (Bohn et al., 2015). However, the SWAMPS v3.2 data set, which serves as a proxy of temporal variations in wetland extent, has had discontinuity issues over a few tropical hotspots since 2015 and hence affects the temporal variations in WAD2M. Consequently, this affects CH₄ emissions estimates for a subset of land surface models that are particularly sensitive to inundation in these hotspots. Meanwhile, prognostic estimates show moderate consistency in capturing the spatial distribution of wetland areas with WAD2M, with an annual average wetland area of 8.0 ± 2.0 Mkm² during the peak season for 2000–2020. The ensemble mean of annual wetland area anomaly by the prognostic models shows reasonable agreement with satellite-based estimates in capturing the response of wetland area to climate variations (Zhang et al., 2025), with higher agreement over temperate and boreal regions than in the tropics.

For the wetland methane emissions estimate, we use the decadal mean from the prognostic runs and adjust these flux estimates for double counting from inland waters (described in next section) given the reliance of the prognostic models on satellite flooded area data like WAD2Mv2 to parameterise maximum wetland extent (Zhang et al., 2025). The average emission from wetlands for 2010–2019 for the 16 models is plotted in Fig. 3. The zones with the largest emissions are the Amazon basin, equatorial Africa and Asia, Canada, west-
ern Siberia, eastern India, and Bangladesh. Regions where CH₄ emissions have high intermodel agreement (defined as regions where mean flux is larger than the standard deviation of the models, on a decadal mean) represent 72% of the total CH₄ flux due to natural wetlands. The different sensitivities of the models to temperature, vapour pressure, precipitation, and radiation can generate substantially different patterns, such as in India. Emission estimates over regions with lower emissions (in total) are also consistently inferred between models (e.g. Scandinavia, continental Europe, eastern Siberia, central USA, and southern Africa).

The resulting global flux range for vegetated wetland emissions from the prognostic runs is 117–195 Tg CH₄ yr⁻¹ for the 2000–2020 period, with an average of 157 Tg CH₄ yr⁻¹ and a 1σ standard deviation of 24 Tg CH₄ yr⁻¹. Using the prognostic set of simulations, the average ensemble emissions were 159 [119–203] Tg CH₄ yr⁻¹ for the 2010–2019 period (Table 3). The estimated average ensemble annual total from the two sets of simulations by CRU/CRU-JRA55 and GSWP3-W5E5 are 158 [126–193] and 158 [118–203] for 2010–2019 respectively. Generally, the magnitude and interannual variability agree between these two sets of simulations (Zhang et al., 2025). Wetland emissions represent about 25% of the total (natural plus anthropogenic) CH₄ sources estimated by bottom-up approaches. The large range in the estimates of wetland CH₄ emissions results from difficulties in defining wetland CH₄-producing areas as well as in parameterising terrestrial anaerobic conditions that drive sources and the oxidative conditions leading to sinks (Melton et al., 2013; Poulter et al., 2017; Wania et al., 2013). The ensemble mean emission using the same simulation set-up (i.e. diagnostic wetland extent and CRU/CRU-JRA55) in the models is 163 [117–195] Tg CH₄ yr⁻¹, higher by ~22 Tg CH₄ yr⁻¹ than the one previously reported (see Table 3, for 2000–2009 with comparison to Saunois et al., 2020). This difference is mainly due to the updated model structure and parameterisations in the wetland CH₄ models compared to the versions in the previous budget and the inclusion of three new land surface models.

For the last decade 2010–2019, we report in this budget an average ensemble estimate of 159 Tg CH₄ yr⁻¹ with a range of 119–203 (based on prognostic wetland extent runs; Table 3).

### 3.2.2 Inland freshwater ecosystems (lakes, ponds, reservoirs, streams, rivers)

This category includes CH₄ emissions from freshwater systems (lakes, ponds, reservoirs, streams, and rivers). Numerous advances have been made in the freshwater greenhouse gas knowledge base in the last few years (Lauerwald et al., 2023a). These advances include improvements in the underlying databases used to estimate inland water surface areas and model their dynamics, a rapidly growing number of di-

rect measurements of methane fluxes, and improvements in our process-based understanding of methane biogeochemistry. Despite this, aspects of global freshwater methane estimates remain rather crude and continue to have large uncertainties. This includes the overall temperature dependency of methane emissions, the relative role of ebullition (i.e. bubble flux, which may represent the most important but most difficult-to-capture emission path in many standing water bodies), fluxes from the smallest standing water bodies (sometimes referred to as ponds) having large emissions per square metre but uncertain area extent, and the magnitude of anthropogenic influence on emissions, all which are discussed below.

#### Streams and rivers

The last global CH₄ budget used an estimate of 27 Tg CH₄ yr⁻¹ for global streams and rivers based largely on a data compilation by Stanley et al. (2016). This estimate was scaled from a simple data compilation without a spatial component or an estimate of ebullition. More recently, Rosentreter et al. (2021) performed a new data compilation of 652 flux estimates, including diffusive and ebullition fluxes, coupled to an ice-corrected surface area estimate of ~625 000 km² that was aggregated to five latitudinal bands to come up with a global estimate of 6 and 31 ± 17 Tg CH₄ yr⁻¹ (respectively for the median and mean ± CI 95%). We believe, due to better data representation in underlying data sets, that the mean estimate of Rosentreter et al. (2021) is more representative statistically because the median does not capture hotspots and hot moments of intense ebullition fluxes. Finally, Rocher-Ros et al. (2023) used a new Global River Methane (GRiMeDB) database (Stanley et al., 2023) with > 24 000 observations of CH₄ concentrations to predict ~28 ± 17 Tg CH₄ yr⁻¹ (± CI 95%) river emissions globally. This approach used machine learning methods coupled to the latest spatially and temporally explicit mapping of monthly stream surface area (the smallest streams are still extrapolated), which incorporates drying and freezing effects (yearly average 672 000 km²; Liu et al., 2022) and includes an ebullition flux estimated from a correlation between measured diffusive and ebullition emissions in the GRiMeDB database (Stanley et al., 2023). Thus, for this study we use an estimate of 29 ± 17 (± CI 95%) Tg CH₄ yr⁻¹ for streams and rivers (Fig. 4), which averages the mean estimate of Rosentreter et al. (2021) and Rocher-Ros et al. (2023). Currently, ebullition fluxes remain a major unknown quantity in streams and rivers but appear to be coarsely linearly correlated in a log space to diffusive fluxes and of similar magnitude (Rocher-Ros et al., 2023). Methodologically, the high-water velocity of many streams and rivers makes measurement of ebullition fluxes challenging (Robison et al., 2021). Effluxes are also linked to hydrology (Aho et al., 2021), although very few studies have sampled over a representative hydrograph.
Plant-mediated effluxes of CH₄ in running waters also remain difficult to constrain, with a recent compilation highlighting very few measurements (Bodmer et al., 2024). Connected adjacent wetlands are a common source of CH₄ to streams and rivers (Borges et al., 2019), which may be important for the regulation of running water emissions but is currently difficult to assess at the global scale. Overall, the poor representation of sites and deficient mechanistic understanding make it difficult to model and predict methane evasion from streams and rivers using process-based models.

## Lakes and ponds

The previous global CH₄ budget used an estimate of 71 Tg CH₄ yr⁻¹ for lakes and 18 Tg CH₄ yr⁻¹ for reservoirs. These estimates were based on an early study by Bastviken et al. (2011) coupled with a newer estimate for lakes north of 50°N (Wik et al., 2016b). There have been three new lake studies that have published their data with global estimates of 56 and 151 ± 73 (Rosentreter et al., 2021; respectively for the median and mean ± CI 95 %, 22 ± 8 (Zhuang et al., 2023; ± lake-area-weighted normalised RMSE for all parameterised lake types), process-based model) and 41 ± 36 Tg CH₄ yr⁻¹ (Johnson et al., 2022a, mean ± CI 95 %). This large range in estimated emissions can be attributed to the differences in the data sets and methods used to calculate the surface area of small waterbodies, as well as the differences between how the flux data were analysed and extrapolated between studies. For instance, total surface areas of all lakes and ponds of 3712–5688 × 10³ km² (Rosentreter et al., 2021) and 2806 × 10³ km² (Johnson et al., 2022a) were used along with measurement data from 198 and 575 individual lake systems respectively. In contrast, Zhuang et al. (2023) generated estimates using higher-temporal-resolution data from just 54 lakes to build a process-based model, which generated much lower flux estimates from tropical lakes than previously implemented statistical approaches, but in line with the most recent assessments by Borges et al. (2022). For this study, we explicitly excluded lakes < 0.1 km², which are treated separately (see below). If we re-assess these three studies for only lakes greater than 0.1 km², we obtain global effluxes of 17 and 42.9 ± 20.8 Tg CH₄ yr⁻¹ (Rosentreter et al., 2021; median and mean ± CI 95 % of global flux), 21.9 ± 8.0 (Zhuang et al., 2023, ± lake-area-weighted normalised RMSE for all parameterised lake types), and 35.3 ± 31.0 Tg CH₄ yr⁻¹ (Johnson et al., 2022a, b, ±95 % CI) (with areas of 2556–3468 × 10³, 2640 × 10³, and 2676 × 10³ km² respectively). Thus, for lakes > 0.1 km², we propose an efflux of 33 ± 26 Tg CH₄ yr⁻¹ (an average of the mean from Rosentreter et al., 2021, Zhuang et al., 2023, and Johnson et al., 2022a, b, with the average CI 95 % from Rosentreter et al., 2021, and Johnson et al., 2022a, b) as represented in Fig. 4.

Small waterbody emissions, hereafter small lakes and ponds < 0.1 km², remain difficult to assess. Evidence is emerging that there is a lower limit to the power scaling laws that early studies used to extrapolate the surface area of these small systems (Bastviken et al., 2023; Kyzivat et al., 2022). Thus, for small lakes and ponds < 0.1 km² (and > 0.001 km²), we disregard the higher end surface area used in Rosentreter et al. (2021), which relied on these earlier estimates and scale their numbers to the evasion estimates to the lower-end surface area of 1002 × 10³ to obtain a mean flux of 33 Tg CH₄ yr⁻¹ (Rosentreter et al., 2021). Johnson et al. (2022a) estimated a surface area of only 166 × 10³ km² for this size class to obtain an efflux of 6.3 Tg CH₄ yr⁻¹, which we acknowledge as a lower limit. Averaging these two values provides a conservative estimate of 20 [6–33] Tg CH₄ yr⁻¹, which is close to the number proposed by Holgerson and Raymond (2016) for diffusion effluxes only for this size class. The experts involved in this assessment have low confidence in this estimate. This estimate also does not include artificial ponds, which we discuss below. As a result, combined CH₄ emissions from large lakes (> 0.1 km²) and small lakes and ponds (< 0.1 km²) are estimated at 53 [19–86] Tg CH₄ yr⁻¹ (Fig. 4), which is lower than the 71 Tg estimated in the previous budget.

## Reservoirs

New mean estimates of diffusive + ebullition CH₄ emissions from reservoirs include 15 and 24 ± 8 (the median and mean ± CI 95 % from Rosentreter et al., 2021), 10 ± 4 (Johnson et al., 2021, mean ± 95 % CI), 10 (Harrison et al., 2021, low and high 95 % CI of 7 and 22 respectively), and 2.1 Tg CH₄ yr⁻¹ (Zhuang et al., 2023). We compile the first three estimates to a direct efflux of ~ 14 Tg CH₄ yr⁻¹ (with ± CI 95 % of 9 and 23). We note the fourth estimate as a lower bound but exclude it from this budget given that it was generated via a model that only included data from six reservoir systems (Zhuang et al., 2023). We also add in an additional 12 Tg CH₄ yr⁻¹ (low and high 95 % CI of 7 and 37 respectively) that is estimated to degas in dam turbines (Harrison et al., 2021), which was not addressed in the studies by Rosentreter et al. (2021), Zhuang et al. (2023), or Johnson et al. (2021). Rocher-Ros et al. (2023) also excluded river observations below dams when executing their statistical model and so did not capture downstream dam emissions. Thus, we use a direct reservoir emission here of ~ 13 [6–28] Tg CH₄ yr⁻¹ and estimate an additional ~ 12 [7–37] Tg CH₄ yr⁻¹ from dam turbine degassing fluxes, giving a total of 25 [13–65] Tg CH₄ yr⁻¹ from reservoirs (Fig. 4).

## Uncertainties and confidence levels

The emission estimates of lakes, reservoirs, and ponds described above are limited by several uncertainties. First, a major unknown for lakes remains the size cut-off and the rep-
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Estimation of wetland and inland freshwater emissions over the 2010–2019 decade (in Tg CH₄ yr⁻¹). The fluxes related to voluntary (such as through reservoirs or farm ponds) or involuntary (land use or eutrophication-related) perturbations of the methane cycle are shown here in pink. They are accounted for in the “natural and indirect anthropogenic” sources in the Table 3 budget and depicted as “natural and indirect anthropogenic” sources (darker green and pink hatches) in Fig. 7. Infographic designed by WeDoData (https://wedodata.fr, last access: 1 April 2025).</figcaption></figure>

representation of small lakes and ponds (Deemer and Holgerson, 2021), which are also more variable than larger water bodies in their CH₄ concentrations and fluxes (Rosentreter et al., 2021; Ray et al., 2023). Interestingly, there is also a lack of methane data representation from large lakes that are a large component of global lake surface area (Deemer and Holgerson, 2021; Messager et al., 2016). There is also a growing knowledge base on the importance of high CH₄ fluxes from lake littoral zones that is not yet well incorporated into global

scaling efforts (e.g. Grinham et al., 2011; Natchimuthu et al., 2016) and emergent vegetation (Bastviken et al., 2023; Kyzivat et al., 2022). Ebullition is more constrained in lakes/reservoirs compared to streams/rivers but is still difficult to measure and model accurately. Finally, for all inland water systems a greater scrutiny for the limiting factors (including the impact of ice cover and seasonality, stratification of the water column) of different CH₄ production, consumption, and transport pathways is needed. In addition, a better under-
standing of the climatic, environmental, and geomorphological controls on key CH₄ processes (e.g. sedimentary diffusive and ebullition production, bubble dissolution, CH₄ oxidation) on the large-scale remains critically needed. For instance, the consistently lower global emissions determined by the process-based model of Zhuang et al. (2023) compared to observations suggest that current data sets are too limited to fully capture the spatiotemporal variability in CH₄ dynamics and their key control factors, possibly leading to biased-high estimates.

The majority of the inland water CH₄ estimates are from a limited number of studies, some without spatial representation or reported statistical uncertainties. Furthermore, as mentioned above the knowledge base of the surface area of these ecosystems is new and rapidly expanding but not standardised between studies leading to uncertainty (but see Lauerwald et al., 2023b), particularly for ponds. For this study, we are able to provide confidence intervals from the original studies for all fluxes except the smallest lake/pond size class.

## The surface area of inland freshwaters

For all of these ecosystems, determining their surface area remains a central challenge. Since the last GMB, several methodological advances have reduced the uncertainty associated with the surface area estimates of rivers, streams, lakes, and reservoirs. Using a single geospatial data set that includes both lakes and reservoirs (Messager et al., 2016) has decreased double counting of lakes and reservoirs (Johnson et al., 2022a; Rosentreter et al., 2021). For rivers and streams, high-resolution global streamflow simulations, informed by satellite observations, enabled a much finer-scale estimate of surface areas for rivers with a new temporal component (Allen and Pavelsky, 2018; Lin et al., 2019; Liu et al., 2022), although the surface for the smaller streams are still estimated indirectly, and mapping of human-created drainage ditches and canals is lacking. Seasonal ice cover and melt turnover corrections also have been newly incorporated into rivers, streams, lakes, and reservoirs (Harrison et al., 2021; Johnson et al., 2022a; Lauerwald et al., 2023b; Rocher-Ros et al., 2023; Rosentreter et al., 2021; Zhuang et al., 2023). Finally, removing open-water-body surface areas from wetland surface areas based on geographic location has reduced double counting between these two land cover types, as described in the wetlands section of the GMB. However, the surface area of small lakes and ponds (<0.1 km²) is still highly uncertain, and new techniques for counting these systems and determining the overlap with wetland data bases are paramount.

## Anthropogenic contributions to inland freshwater emissions

We argue that all reservoirs should be categorised as a direct anthropogenic source of emissions. Most of the surface area of reservoirs are human-made, and reservoir construction leads to anoxic sediments and/or bottom waters with labile organic matter sourced from the watershed and to in situ nutrient augmented phytoplankton production (Deemer et al., 2016; Maavara et al., 2017; Prairie et al., 2018). It is also clear that the cultural eutrophication of natural lakes driven by run-off of agricultural nitrogen fertiliser and manure amendments CH₄ emissions (DelSontro et al., 2018; Li et al., 2021), with shallow lakes particularly likely to experience eutrophication (Qin et al., 2020). For instance, Beaulieu et al. (2019) modelled a 15% reduction in lake CH₄ with a 25% reduction in lake phosphorus concentrations. Several recent studies have estimated that anywhere between 30% and 50% of lakes are eutrophic (Cael et al., 2022; Qin et al., 2020; Sayers et al., 2015; Wu et al., 2022). These studies estimate numerical percentages (one by depth class: Qin et al., 2020), but none have estimated the percent of lake surface area that is eutrophic nor have any determined the extent of anthropogenic vs. natural eutrophication. Still, numerous studies have noted widespread increases in eutrophication indicators across lakes due to nutrient loading and warming (Griffiths et al., 2022; Ho et al., 2019; Taranu et al., 2015); thus we estimate that one-third or 11 Tg CH₄ yr⁻¹ of CH₄ emissions from lakes > 0.1 km² could be anthropogenic (Fig. 4). Similarly, CH₄ emissions from small lakes and ponds are influenced by human factors, with emissions increasing with eutrophication (Deemer and Holgerson, 2021), erosion and runoff in agricultural landscapes (Heathcote et al., 2013), and warming, the latter likely to have a disproportionately greater effect in small, shallow systems (Woolway et al., 2016). Thus, we adopt the same 1/3 number as for lakes for the proportion of anthropogenic emissions in small lakes and ponds (<0.1 km²), which amounts to 6 Tg CH₄ yr⁻¹ (Fig. 4).

There are also human-made small lakes and ponds, notably for agriculture, aquaculture, and recreation, that generally have conditions favourable for high CH₄ emissions (Downing, 2010; Holgerson and Raymond, 2016; Malerba et al., 2022; Ollivier et al., 2019; Zhao et al., 2021; Dong et al., 2023). Downing (2010) estimated that farm ponds comprise a global surface area of ~77 000 km²; using a conservative emission rate of 265 mg CH₄ m⁻² d⁻¹ and an ice correction factor of 0.6 leads to an emission of 4.5 Tg yr⁻¹ that is anthropogenically sourced from farm ponds. Here the value is rounded to 5 Tg yr⁻¹ (Fig. 4). Clearly, more work is required to assess the anthropogenic component of CH₄ emissions from lakes and ponds.

It remains difficult to parse out an anthropogenic component to stream and river CH₄ fluxes. Although some studies have noticed a temperature dependence with stream sediments (Comer-Warner et al., 2018; Zhu et al., 2020), Rocher-
Ros et al. (2023) noted a small temperature dependence of CH₄ emissions in streams and rivers compared to other freshwater ecosystems, potentially due to the many other external processes affecting fluxes in these dynamic flowing ecosystems. Urbanisation can lead to elevated river CH₄ emissions, particularly in regions with elevated organic matter and nutrient loading due to limited wastewater treatment (Begum et al., 2021; Nirmal Rajkumar et al., 2008; Wang et al., 2021a). Some studies have found agricultural streams and ditches can have higher effluxes due to inputs of fine sediments (Comer-Warner et al., 2018; Crawford and Stanley, 2016), organic carbon, and nutrients (Borges et al., 2018) that lead to in situ methane production. Furthermore, the creation of drainage ditches in organic soils taps CH₄-rich waters from water-logged horizons and increases the emissions from ex situ sources (Peacock et al., 2021). However, limitations in both the geographic coverage of existing ditch emission estimates and our ability to estimate the global surface area of ditches preclude their inclusion in this budget. Finally, extremely high rates of CH₄ emission have been linked to ongoing permafrost thaw in Asia's Qinghai–Tibet Plateau (Zhang et al., 2020). However, the loss and disconnection of wetlands to rivers may have resulted in a decrease in the input of dissolved CH₄ from this source. A recent expert elicitation (Rosentreter, et al., 2024) reported that 35% of all inland freshwater sources were anthropogenic; given that some of the river flux is from upstream reservoirs, we assign a 30% anthropogenic contribution to the stream and river flux (9 Tg CH₄ yr⁻¹, Fig. 4), which approximates the expert elicitation via the impact of eutrophication and urban influences.

## Combination (lakes, ponds, reservoirs, streams and rivers, farm ponds)

Combining the aforementioned emissions from lakes > 0.1 km² (33 [13–53] Tg CH₄ yr⁻¹), small lakes and ponds < 0.1 km² (20 [6–33] Tg CH₄ yr⁻¹), reservoirs (25 [13–65] Tg CH₄ yr⁻¹), streams and rivers (29 [12–46] Tg CH₄ yr⁻¹) and farm ponds (5 Tg CH₄ yr⁻¹) leads to a total of ~ 112 Tg CH₄ yr⁻¹ from freshwater systems, with a range of [49–202] Tg CH₄ yr⁻¹. This estimate is about 50 Tg lower than in Saunois et al. (2020) and is broadly consistent with the recent regionalised estimate by Lauerwald et al. (2023b) compiled for the Regional Carbon Cycle Assessment and Processes (RECCAP2, https://www.globalcarbonproject.org/reccap/, last access: 1 April 2025; 103 Tg CH₄ yr⁻¹, IQR = 82.1–134.8). The updated budget from these ecosystems and their anthropogenic components are represented in Fig. 4. The gridded products for emissions from lakes and ponds by Johnson et al. (2022b), from reservoirs by Johnson (2021), and from streams and rivers by Rocher-Ros (2023) have been combined into a single map presented in Fig. 5.

## Double counting inland freshwater ecosystems in the bottom-up estimates

To address the differences found between bottom-up and top-down CH₄ budgets, and to acknowledge advances in addressing the central issue of double counting CH₄ emissions for inland freshwater ecosystems, we introduce here a new correction term. Historically, the bottom-up estimate of global CH₄ emissions has been higher than the top-down estimate, first recognised in Kirschke et al. (2013) and confirmed in Saunois et al. (2016, 2020). The larger bottom-up emissions estimate has been partly attributed to double counting vegetated wetland emissions with inland freshwater emissions (including lakes, ponds, rivers, streams, and reservoirs) and the emissions of CH₄ produced in vegetated wetlands and then transported via aquatic processes and emitted from inland freshwaters (Pangala et al., 2017; Kirk and Cohen, 2023). The Saunois et al. (2020) CH₄ budget addressed the issue of double counting through the use of a revised vegetated wetland area data set, WAD2M v1.0 (Zhang et al., 2021a), which removed inland waters from the SWAMPS (Jensen and McDonald, 2019) surface–inundation data set, allowing for independent vegetated wetlands and inland freshwater CH₄ emissions to be compiled. However, the Saunois et al. (2020) CH₄ budget still had a ~150 Tg CH₄ yr⁻¹ difference between bottom-up and top-down estimates. In this budget, we refined the vegetated wetland area data set with WAD2M v2.0 (see Sect. 3.2.1, where HydroLAKES is used to remove lakes and ponds > 0.1 km²). Additionally, we applied numbers from peer-reviewed publications and expert elicitation to account for lateral CH₄ flux emissions. This most recent bottom-up budget estimates 159 [119–203] Tg CH₄ yr⁻¹ from vegetated wetlands for 2010–2019 and 112 Tg CH₄ yr⁻¹ from inland freshwaters, which includes 83 Tg CH₄ yr⁻¹ from lakes, ponds, and reservoirs and 29 Tg CH₄ yr⁻¹ from rivers and streams, leading to a combined wetland and inland freshwater flux of 271 Tg CH₄ yr⁻¹. Here, we propose a correction of 20 Tg CH₄ yr⁻¹ to account for double counting of small lakes and ponds (< 0.1 km²) that are likely included in our vegetated wetlands estimate and removing 1–3 Tg CH₄ yr⁻¹ from river emissions due to lateral transport of CH₄ originating in adjacent vegetated wetlands. The river flux correction arises from assuming that for catchments with > 10% wetlands, rivers provide 5%–10% of vegetated CH₄ emissions. The total double counting correction term of 23 Tg CH₄ reduces the bottom-up budget for combined wetlands and inland waters from 271 to 248 Tg CH₄ yr⁻¹ (see Fig. 4 and Table 3). Comparing the 2000–2009 decadal emissions from wetlands and inland freshwater ecosystems across the last three previous assessments of the budget shows a significant 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).
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Methane emissions (mg CH₄ m⁻² d⁻¹) from four natural and indirect anthropogenic sources: inland freshwaters (including lakes, ponds (Johnson et al., 2022b), reservoirs (Johnson et al., 2021; Johnson, 2021), and stream and rivers (Rocher-Ros et al., 2023; Rocher-Ros, 2023) with a global total scaled to 89 Tg yr⁻¹), geological sources (Etiope et al., 2019), termites (this study), and oceans (Weber et al., 2019).</figcaption></figure>

Finally, it is worth noting that inland freshwater ecosystems can overlap with geological seepage systems in some areas; i.e. they may occur in correspondence with geological structures that emit fossil (microbial, thermogenic, or abi-otic) CH₄ generated in the Earth's crust. Examples have been documented in the Fisherman Lake in Canada (Smith et al., 2005), in Lake Baikal (Schmid et al., 2007), and in rice pad-dies in Japan (Etiope et al., 2011). Thus, some gas emissions in freshwater environments, particularly as bubble plumes, can be incorrectly attributed to modern biological (ecosys-tem) activities if appropriate isotopic and molecular analyses are not performed.

### 3.2.3 Onshore and offshore geological sources

Significant amounts of CH₄, produced within the Earth's crust, naturally migrate to the atmosphere through tectonic faults and fractured rocks. Major emissions are related to hy-drocarbon formation in sedimentary basins (microbial and thermogenic methane), through continuous or episodic ex-halations from onshore and shallow marine hydrocarbon seeps and through diffuse soil microseepage (Etiope, 2015). Specifically, five source categories have been considered. Four are onshore sources: gas-oil seeps, mud volcanoes, dif-fuse microseepage, and geothermal manifestations includ-ing volcanoes. One source is offshore: submarine seepage, which may include the same types of gas manifestations oc-curring on land. Etiope et al. (2019) have produced the first gridded maps of geological CH₄ emissions and their iso-topic signature for these five categories, with a global to-tal of 37.4 Tg CH₄ yr⁻¹ (reproduced in Fig. 5). However, these maps are based on incomplete data on geological sites due to missing information and difficulties in defining all current geological emitting sites. Combining the best esti-mates for the five categories of geological sources (from grid

maps or from previous statistical and process-based mod-els), the breakdown by category reveals that onshore mi-croseepage dominates (24 Tg CH₄ yr⁻¹), with the other cat-egories having similar smaller contributions: as mean val-ues, 4.7 Tg CH₄ yr⁻¹ for geothermal manifestations, about 7 Tg CH₄ yr⁻¹ for submarine seepage, and 9.6 Tg CH₄ yr⁻¹ for onshore seeps and mud volcanoes. These values lead to a global bottom-up geological emission mean of 45 [27–63] Tg CH₄ yr⁻¹ (Etiope and Schwietzke, 2019).

While all bottom-up and some top-down estimates, fol-lowing different and independent techniques from differ-ent authors, consistently suggest a global geo-CH₄ emission on the order of 40–50 Tg yr⁻¹, the radiocarbon (¹⁴C–CH₄) data in ice cores reported by Hmiel et al. (2020) appear to give a much lower estimate, with a minimum of about 1.6 Tg CH₄ yr⁻¹ and a maximum value of 5.4 Tg CH₄ yr⁻¹ (95 % confidence) for the pre-industrial period. Dyonisius et al. (2020) also suggest a low range of geological emis-sions over the last deglaciation period and for the Late Holocene (0–10 Tg CH₄ yr⁻¹). The discrepancy between Hmiel et al. (2020) and all other estimates has been dis-cussed in Thornton et al. (2021), which demonstrated that the global near-zero geologic CH₄ emission estimate in Hmiel et al. (2020) is incompatible with the sum of multiple indepen-dent bottom-up estimates, based on a wide variety of method-ologies, from individual natural geological seepage areas: for example, from the Black Sea (up to 1 Tg CH₄ yr⁻¹), the East-ern Siberian Arctic Shelf (ESAS; up to 4.6 Tg CH₄ yr⁻¹, re-ferring mostly to thermogenic gas), onshore Alaska (up to 1.4 Tg CH₄ yr⁻¹), and a single seepage site in Indonesia (re-leasing 0.1 Tg CH₄ yr⁻¹ as estimated by satellite measure-ment) (see Thornton et al., 2021, and references therein). Jackson et al. (2020) expressed doubt about the low Hmiel et al. (2020) estimates, noting that they are difficult to reconcile
with the results of many other researchers and with bottom-
up approaches in general. This discrepancy highlights an-
other main unresolved uncertainty in the methane budget and
calls for further investigations to reconcile the different es-
timates and reduce the uncertainty on geological emissions.
Waiting for further investigation to better understand discrep-
ancies between radiocarbon approaches and other studies,
we decided to keep the estimates from Etiope and Schwi-
etzke (2019) for the mean values and associate them with
the lowest estimates reported in Etiope et al. (2019), as in
Saunois et al. (2020). Thus, we report a total global geolog-
ical emission of 45 [18–63] Tg CH₄ yr⁻¹, with a breakdown
between offshore emissions of 7 [5–10] Tg CH₄ yr⁻¹ and on-
shore emissions of 38 [13–53] Tg CH₄ yr⁻¹ (Table 3), similar
to Saunois et al. (2020). This bottom-up estimate is slightly
lower than in the Saunois et al. (2016) budget mostly due to
a reduction of estimated emissions of onshore and offshore
seeps (see Sect. 3.2.6 for more offshore contribution expla-
nations).

3.2.4 Termites

Termites are decomposers playing a central role in ecosystem nutrient fluxes at tropical and subtropical latitudes, in particular (Abe et al., 2000). Termites represent a natural CH4 source due to methanogenesis occurring in their hindgut during the symbiotic metabolic breakdown of lignocellulose (Sanderson, 1996; Brune, 2014). The upscaling of CH4 emissions from termites from site to global level is characterised by high uncertainty (Sanderson, 1996; Kirschke et al., 2013; Saunois et al., 2016) due to the combination of factors that need to be considered and the scarcity of information for each of these factors for global upscaling. Needed data include termite biomass density (Sanderson, 1996), species distribution within and among ecosystems (Sugimoto et al., 1998), variation in termite CH4 emission rates per species and dietary group (Sanderson, 1996), and the role played by the termite mound structure in affecting the fraction of produced CH4 that is effectively released into the atmosphere (Sugimoto et al., 1998; Nauer et al., 2018). In Kirschke et al. (2013) and Saunois et al. (2016), a global upscaling of termite CH4 emissions was proposed, where CH4 emissions, ECH4 (kg CH4 ha⁻¹ yr⁻¹), were estimated as the product of three terms: termite biomass (Bioterm g fresh weight m⁻²), a scalar correction factor (LU) expressing the effect of land use/cover change on termite biomass density, and a termite CH4 emission factor (EFterm, µg CH4 g⁻¹ BioTerm h⁻¹). The approach between the two re-analyses of CH4 emissions varied only for the data sources of gross primary productivity (GPP) and land use, which were used to attribute biomass values of termite per ecosystem surface unit, in order to cover different time spans: 1980s, 1990s, and 2000s in Kirschke et al. (2013) and 2000–2007 and 2010–2016 in Saunois et al. (2016). For the present update, additional changes have been introduced compared with the previous versions. Here

we summarise the key data used for the new upscaling. CH4
fluxes were modelled between 45° S and 45° N and within
35° S and 35° N. The termite biomass density, Bioterm, for
tropical ecosystems was estimated as a function (Kirschke et
al., 2013; Bioterm = 1.21 × exp(0.0008 · GPP)) of the gross
primary production (GPP; g C m⁻² yr⁻¹) using the 0.25° na-
tive resolution VODCA2GPP data set covering the period
2001–2020 (Wild et al., 2022). Wetlands, barren areas, water
bodies, and artificial surfaces were excluded from this es-
timation and set as no data (no emissions). The scalar cor-
rection factor LU of 0.4 (60%) for agricultural areas (i.e.
croplands) (Kirschke et al., 2013) was applied to the GPP
value of the nearest natural areas to account for anthropic
disturbance. The annual (2001–2020) land cover informa-
tion was obtained from the MODIS Terra+Aqua Combined
Land Cover product (MCD12C1v006; https://lpdaac.usgs.
gov/products/mcd12c1v006/, last access: 1 April 2025), us-
ing the International Geosphere-Biosphere Programme (IGP)
classification with a 0.05° spatial resolution. For desert and
arid lands, within 35° S and 35° N, a fixed Bioterm value
of 1.56 g m⁻² was instead used (Sanderson, 1996; Heděnec
et al., 2022). Similarly, fix values from the few available
studies reported in literature were used to estimate Bioterm
between 35–45° N and 35–45° S as follows: 1.83 g m⁻² for
temperate forests and grasslands (Wood and Sands, 1978;
Petersen and Luxton, 1982; Sanderson, 1996; Bignell and
Eggleton, 2000; King et al., 2013; conversion factor from
dry to fresh biomass is 0.27 from Petersen and Luxton,
1982), 5.3 g m⁻² for shrublands and Mediterranean areas of
Australia (Sanderson, 1996), and 1.09 g m⁻² for the other
Mediterranean shrubland ecosystems (Heděnec et al., 2022).
Other climates and land covers were set as no data. Climate
zoning was defined using the Köppen–Geiger climate zone
data set (Beck et al., 2018); this product is representative of
the 1980–2016 time period and has a 0.0083° native resolu-
tion. The EFterm was revised compared with previous esti-
mates (Kirschke et al., 2013; Saunois et al., 2016), in order
to consider the different distribution of termite families and
subfamilies in the different continents and ecosystems, char-
acterised by different feeding habits and nest typologies, as
reported by Sugimoto et al. (1998), which might influence
the EF. The species of each family and subfamily of the two
major groups of lower and higher termites, listed by Sugi-
moto et al. (1998), were associated with EF values based on
emissions from in vitro experiments as reported by Sander-
son (1996) and Eggleton et al. (1999), to which a correction
factor (cfMOUND) of 0.5 (Nauer et al., 2018; Chiri et al., 2020,
2021) was applied in order to take into account the mound ef-
fect on the CH4 produced by termites, once inside the nest.
The average EFterm for tropical and temperate areas was
hence estimated as the weighted EFterm derived from the
product of the percentage weight of each family or subfam-
ily of termites in the “community composition” in each ge-
ographical area and ecosystem (Sugimoto et al. (1998; Ta-
ble 6), the respectively calculated EF of each family or sub-
<table><caption>**Table 4.** Top-down studies used here with their contribution to the decadal and yearly estimates noted. For decadal means, top down studies must provide at least 8 years of data over the decade to contribute to the estimate. Details on each inverse system and inversions are provided in Tables S8 to S11 in the Supplement.</caption><thead><tr><th>Model</th><th>Institution</th><th>Observation used</th><th>Time period</th><th>Number of inversions</th><th>2000–2009</th><th>2010–2019</th><th>2020</th><th>References</th></tr></thead><tbody><tr><td>Carbon Tracker – Europe CH<sub>4</sub></td><td>FMI</td><td>Surface stations</td><td>2000–2020</td><td>4</td><td>y</td><td>y</td><td>y</td><td>Tsuruta et al. (2017)</td></tr><tr><td>LMDz-CIF</td><td>LSCE/CEA</td><td>Surface stations</td><td>2000–2020</td><td>4</td><td>y</td><td>y</td><td>y</td><td>Thanwerdas et al. (2022a)</td></tr><tr><td>LMDz-PYVAR</td><td>LSCE/CEA/THU</td><td>GOSAT Leicester v9.0</td><td>2010–2020</td><td>4</td><td>n</td><td>y</td><td>y</td><td>Zheng et al. (2018a, b, 2019)</td></tr><tr><td>MIROC4-ACTM</td><td>JAMSTEC</td><td>Surface stations</td><td>2000–2020</td><td>5</td><td>y</td><td>y</td><td>y</td><td>Patra et al. (2018); Chandra et al. (2021)</td></tr><tr><td>NISMON-CH<sub>4</sub></td><td>NIES/MRI</td><td>Surface stations</td><td>2000–2020</td><td>2</td><td>y</td><td>y</td><td>y</td><td>Niwa et al. (2022, 2024)</td></tr><tr><td>NIES-TM-FLEXPART (NTFVAR)</td><td>NIES</td><td>Surface stations</td><td>2000–2020</td><td>2</td><td>y</td><td>y</td><td>y</td><td>Maksyutov et al. (2021), Wang et al. (2019a)</td></tr><tr><td>NIES-TM-FLEXPART (NTFVAR)</td><td>NIES</td><td>GOSAT NIES L2 v02.95</td><td>2010–2020</td><td>1</td><td>n</td><td>y</td><td>y</td><td>Maksyutov et al. (2021), Wang et al. (2019a)</td></tr><tr><td>TM5-CAMS</td><td>TNO/VU</td><td>Surface stations</td><td>2000–2020</td><td>1</td><td>y</td><td>y</td><td>y</td><td>Segers et al. (2022)</td></tr><tr><td>TM5-CAMS</td><td>TNO/VU</td><td>GOSAT ESA/CCI v2.3.8 (combined with surface observations)</td><td>2010–2020</td><td>1</td><td>n</td><td>y</td><td>y</td><td>Segers et al. (2022)</td></tr><tr><td>Total number of runs</td><td></td><td></td><td></td><td>24</td><td>18</td><td>24</td><td>24</td><td></td></tr></tbody></table>

<table><caption>**Table 5.** Global and latitudinal total methane emissions (in Tg CH$_{4}$ yr$^{-1}$), as decadal means (2000–2009 and 2010–2019) and for the year 2020 from this work using bottom-up and top-down approaches. Global and latitudinal emissions for 2000–2009 are also compared with Saunois et al. (2016, 2020) for top-down and bottom-up approaches when available. Uncertainties are reported as [min–max] range. The mean, minimum, and maximum values are calculated while discarding outliers, for each category of source and sink. As a result, discrepancies may occur when comparing the sum of categories and their corresponding total due to differences in outlier detections. Differences of 1 Tg CH$_{4}$ yr$^{-1}$ in the totals can also occur due to rounding errors. For the latitudinal breakdown, bottom-up anthropogenic estimates are based only on the gridded products (see Table 1). As a result, the total from the latitudinal breakdown (line called “This work (gridded BU products only)”) is slightly different from the values provided in Table 3 and recalled in the line “This work (all BU products)”. BU stands for bottom-up. S2020 and S2016 refer to Saunois et al. (2020) and Saunois et al. (2016) respectively.</caption><thead><tr><th>Period</th><th colspan="2">2000–2009</th><th colspan="2">2010–2019</th><th colspan="2">2020</th></tr><tr><th>Approach</th><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>Global</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>This work (all BU products)</td><td>638 [485–813]</td><td>543 [526–558]</td><td>669 [512–849]</td><td>575 [553–586]</td><td>685 [540–865]</td><td>608 [581–627]</td></tr><tr><td>This work (gridded BU products only)</td><td>642 [501–809]</td><td></td><td>676 [526–845]</td><td></td><td>691 [565–862]</td><td></td></tr><tr><td>S2020</td><td>703 [566–842]</td><td>547 [524–560]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>S2016</td><td>719 [583–861]</td><td>552 [535–566]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>90° S–30° N</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>This work</td><td>367 [254–487]</td><td>337 [311–361]</td><td>388 [275–503]</td><td>364 [337–390]</td><td>395 [292–521]</td><td>386 [353–425]</td></tr><tr><td>S2020</td><td>408 [322–532]</td><td>346 [320–379]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>S2016</td><td>–</td><td>356 [334–381]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>30–60° N</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>This work</td><td>234 [169–335]</td><td>182 [162–197]</td><td>250 [184–345]</td><td>187 [160–204]</td><td>256 [186–356]</td><td>197 [170–215]</td></tr><tr><td>S2020</td><td>252 [202–342]</td><td>178 [159–199]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>S2016</td><td>–</td><td>176 [159–195]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>60–90° N</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>This work</td><td>42 [22–79]</td><td>26 [22–33]</td><td>38 [17–73]</td><td>24 [18–29]</td><td>39 [17–74]</td><td>25 [20–32]</td></tr><tr><td>S2020</td><td>42 [28–70]</td><td>23 [17–32]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>S2016</td><td>–</td><td>20 [15–25]</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
family, and a scalar or correction factor which considers the nest type (as in Table 5 from Sugimoto et al., 1998). For desert and arid lands and temperate areas, which were not reported in Sugimoto et al. (1998), EF rates were calculated directly from data reported in literature for the most representative species, which were the genus *Amitermes* for the former (EF from data by Sanderson, 1996; Eggleton et al., 1999; Jamali et al., 2011) and the genus *Reticulitermes* (fam-ily Rhinotermitidae) for the latter (EF from data by Odel-son and Breznak, 1983; Sanderson, 1996; Eggleton et al., 1999; Myer et al., 2021). The following EF<sub>TERMS</sub> were hence obtained to scale up emissions: $3.26 \pm 1.79 \mu g \ CH_4 \ g^{-1} \ termite h^{-1}$ (28.56 mg $CH_4 \ g^{-1}$ termite $yr^{-1}$) for tropical ecosystems; $1.82 \pm 1.54 \mu g \ CH_4 \ g^{-1} \ termite h^{-1}$ for temperate forests, grasslands, and Mediterranean areas; and $1.24 \pm 1.22 \mu g \ CH_4 \ g^{-1} \ termite h^{-1}$ for deserts and arid lands (warm climate). Annual $CH_4$ fluxes were computed for all the years from 2001 to 2020, producing 20 global maps at 0.05° resolution of yearly total emissions. A further map of the estimated error representative of the entire time period was elaborated at the same resolution as the emissions data set.

Termite $CH_4$ emissions over the period 2001–2020 varied between 9.7–10.8 Tg $CH_4 \ yr^{-1}$, with an average of $10.2 \pm 6.2$ Tg $CH_4 \ yr^{-1}$. Considering a 20-year average, tropical and subtropical moist broadleaf forests contributed to 46% of the total average flux, while tropical and subtropical grasslands, savannas, and shrublands to another 36%. In terms of regional contribution, 37.2% of fluxes were attributed to South America, 31.5% to Africa, 18.1% to Asia, 5.5% to Australia, 7.4% to North America, and less than 1% to Europe. The present estimate value is within the range of previous upscaling studies, spanning from 2 to 22 Tg $CH_4 \ yr^{-1}$ (Ciais et al., 2013). In this study, we report a decadal value of 10 Tg $CH_4 \ yr^{-1}$ with a range of [4–16] (Table 3).

### 3.2.5 Wild animals

Wild ruminants emit $CH_4$ through microbial fermentation that occurs in their rumen, similarly to domesticated livestock species (USEPA, 2010b). Using a total animal population of 100–500 million, Crutzen et al. (1986) estimated the global emissions of $CH_4$ from wild ruminants to be in the range of 2–6 Tg $CH_4 \ yr^{-1}$. More recently, Pérez-Barbería (2017) lowered this estimate to 1.1–2.7 Tg $CH_4 \ yr^{-1}$ using a total animal population estimate of 214 million (range of 210–219), arguing that the maximum number of animals (500 million) used in Crutzen et al. (1986) was poorly justified. Moreover Pérez-Barbería (2017) also stated that the value of 15 Tg $CH_4 \ yr^{-1}$ found in the last IPCC reports is much higher than their estimate because this value comes from an extrapolation of Crutzen's work for the last glacial maximum when the population of wild animals was much larger, as originally proposed by Chappellaz et al. (1993). Re-

cently, based on the modelling of grassland extent, Kleinen et al. (2023) also suggest that the populations of wild animals during the last glacial maximum proposed by Crutzen et al. (1986) and further used by Chappellaz et al. (1993) were overestimated. However, the estimate of 1–3 Tg $CH_4 \ yr^{-1}$ seems underestimated when considering that Hempson et al. (2017) found actual $CH_4$ emissions from African wildlife alone to be around 9 Tg $CH_4 \ yr^{-1}$ but without discussing the uncertainty of this value. As a result, high uncertainty remains and recalls the need for further investigation of this natural source of $CH_4$.

Based on these findings and waiting for further global estimates, the range adopted in this updated $CH_4$ budget is 2 [1–3] Tg $CH_4 \ yr^{-1}$ (Table 3).

### 3.2.6 Coastal and oceanic sources

Coastal and oceanic sources comprise $CH_4$ release from estuaries, coastal vegetated habitats, and marine waters, including seas and oceans. Possible sources of coastal and oceanic $CH_4$ include (1) in situ biogenic production through various pathways in oxygenated sea surface waters (Oremland, 1979; Karl et al., 2008; Lenhart et al., 2016; Repeta et al., 2016), a flux that can be enhanced in the coastal ocean because of submarine groundwater discharge (USEPA, 2010b); (2) production from shallow and marine (bare and vegetated) sediments including free gas or destabilised hydrates and thawing subsea permafrost containing modern ($^{14}$C-bearing) microbial gas; and (3) geological marine seepage (see also Sect. 3.2.3), including hydrates, containing fossil ($^{14}$C-free) microbial or thermogenic $CH_4$. $CH_4$ produced in marine sediments and seabed $CH_4$ seepage can be transported across the water column to the sea surface by upwelling waters (once at the surface methane can cross the sea–air interface via diffusion) and gas bubble plumes (for instance from geological marine seeps; e.g. Judd, 2004; Etiope et al., 2019). Gas bubble plumes generally reach the atmosphere in relatively shallow waters (< 400 m) of continental shelves depending on the intensity of the events (e.g. Westbrook et al., 2009); however massive deep-water seepage events could contribute a significant amount of $CH_4$ to the atmosphere, even from depths > 1000 m (e.g. Schmale et al., 2005.; Greinert et al., 2006; Solomon et al., 2009). In coastal vegetated habitats, $CH_4$ can also be transported to the atmosphere through the aerenchyma of emergent aquatic plants (Purvaja et al., 2004).

We distinguish between coastal and oceanic “geological” and “modern biogenic” $CH_4$ sources. Coastal and oceanic “geological” emissions refer to $CH_4$ seepage from the Earth’s crust (mostly in hydrocarbon-rich sedimentary basins), which is typically evaluated by combining geochemical analyses (isotopic and molecular, including radiocarbon, $^{14}$C, analyses) and geological observations (degassing along faults, seeps, mud volcanoes). Geological emissions do not contain modern biogenic gas that is fossil ($^{14}$C-free). Coastal and oceanic “biogenic” $CH_4$ refers to $CH_4$ formed in situ in
<table><caption>**Table 6.** Latitudinal methane emissions (in Tg CH₄ yr⁻¹) for the last decade 2010–2019, based on top-down and bottom-up approaches. Uncertainties are reported as [min–max] range of reported studies. The mean, minimum, and maximum values are calculated while discarding outliers, for each category of source and sink. As a result, discrepancies may occur when comparing the sum of categories and their corresponding total due to differences in outlier detections. Differences of 1 Tg CH₄ yr⁻¹ in the totals can also occur due to rounding errors. For bottom-up approaches, natural and indirect anthropogenic sources are estimated based on available gridded data sets (see text 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 only on the gridded products (see Table 1).</caption><thead><tr><th rowspan="2">Latitudinal band</th><th colspan="2">90° S–30° N</th><th colspan="2">30–60° N</th><th colspan="2">60–90° N</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>Natural and indirect anthropogenic sources</td><td>178 [95–276]</td><td>148 [133–164]</td><td>100 [43–188]</td><td>42 [36–50]</td><td>28 [9–53]</td><td>14 [10–21]</td></tr><tr><td>Combined wetland and inland freshwaters</td><td>151 [85–234]</td><td>128 [112–155]</td><td>73 [32–147]</td><td>27 [20–42]</td><td>24 [9–53]</td><td>9 [7–17]</td></tr><tr><td>Other natural</td><td>27 [11–42]</td><td>22 [20–29]</td><td>27 [10–41]</td><td>19 [16–22]</td><td>4 [2–6]</td><td>3 [1–5]</td></tr><tr><td>Direct anthropogenic sources</td><td>210 [180–227]</td><td>215 [191–238]</td><td>151 [142–157]</td><td>144 [121–162]</td><td>10 [6–14]</td><td>10 [6–16]</td></tr><tr><td>Agriculture & waste</td><td>140 [121–150]</td><td>150 [135–168]</td><td>81 [77–84]</td><td>77 [56–88]</td><td>1 [1–2]</td><td>2 [2–2]</td></tr><tr><td>Fossil fuels</td><td>52 [44–65]</td><td>46 [36–62]</td><td>65 [61–71]</td><td>61 [50–69]</td><td>7 [4–10]</td><td>7 [3–13]</td></tr><tr><td>Biomass & biofuel burning</td><td>22 [18–30]</td><td>19 [16–21]</td><td>7 [4–10]</td><td>6 [2–7]</td><td>1 [0–1]</td><td>1 [1–2]</td></tr><tr><td>Sum of sources</td><td>388 [275–503]</td><td>364 [337–390]</td><td>250 [184–345]</td><td>187 [160–204]</td><td>38 [7–73]</td><td>24 [18–29]</td></tr></tbody></table>

coastal and marine sediments and in the water column by recent or modern microbial activity (therefore with measurable amounts of radiocarbon (¹⁴C)). To avoid double counting, we assume that all diffusive CH₄ emissions outside of geological seepage regions (identified in global grid maps; Etiope et al., 2019) are fuelled by biogenic CH₄. Finally, we briefly discuss the case of CH₄ hydrates, which can be considered either a “geological” source when they host fossil CH₄ or a “biogenic” source when they host modern CH₄.

## Coastal and oceanic modern biogenic methane emissions

Area-integrated diffusive modern biogenic CH₄ emissions from coastal ecosystems are 1–2 magnitudes lower than from inland freshwaters but significantly higher than biogenic emissions from the open ocean (Rosentreter et al., 2021; Rosentreter et al., 2023; Weber et al., 2019). Particularly the shallow vegetated coastline fringed by mangroves, salt marshes, and seagrasses is a CH₄ hotspot in the coastal ocean, characterised by significantly higher flux densities than other coastal settings such as estuaries or the continental shelves (Rosentreter et al., 2021; Rosentreter et al., 2023). Coastal ecosystems are thus being increasingly recognised as weak global sources to the atmosphere (Weber et al., 2019; Saunois et al., 2020; Rosentreter et al., 2021). Hydrogenotrophic and acetoclastic methanogenesis are largely outcompeted by sulfate reduction in coastal/marine sediments, which is often shown by a decreasing trend

of CH₄ concentrations with increasing salinity from upper tidal (low salinity) to marine (high salinity) regions. Much of the CH₄ produced below the sulfate-reduction zone is indeed re-oxidised by sedimentary anaerobic methane oxidation or re-oxidised in the water column, leading to small emissions despite much larger production (Knittel and Boetius, 2009; Regnier et al., 2011). Methylated compounds such as methylamines and methyl sulfides are non-competitive substrates that are exclusively used by methanogens; therefore, methylated methanogenesis can occur in coastal regions with high sulfate concentrations, for example, in organic-rich (Maltby et al., 2018), vegetated (Schorn et al., 2022), and hypersaline coastal sediments (Xiao et al., 2018). Coastal CH₄ can be driven by the exchange of pore water or groundwater (high in CH₄) with coastal surface waters in tidal systems, referred to as tidal pumping (Ovalle et al., 1990; Call et al., 2015). Anthropogenic impacts such as wastewater pollution and land use change can increase CH₄ fluxes in estuaries (Wells et al., 2020). A large increase of CH₄ emissions follows the conversion of natural coastal habitats to aquaculture farms (Yuan et al., 2019; Yang et al., 2022).

Currently available global modern biogenic CH₄ flux data show high spatiotemporal variability within and between coastal systems because of the overall global paucity of data. Therefore, global estimates have high uncertainties and show large ranges in both empirical (Rosentreter et al., 2021) and machine-learning-based approaches (Weber et al., 2019). According to a recent data-driven metadata analysis, global estuaries, including tidal systems and
deltas, lagoons, and fjords, are estimated to emit a median (Q1–Q3) of 0.25 (0.07–0.46) Tg CH₄ yr⁻¹ (Rosentreter et al., 2023). Coastal vegetation, including mangrove forests, salt marshes, and seagrasses, is estimated to emit 0.77 (0.47–1.41) Tg CH₄ yr⁻¹, which is 3 times more than global estuaries (Rosentreter et al., 2023). The combined median (Q1–Q3) emission of 1.01 (0.54–1.87) Tg CH₄ yr⁻¹ for coastal vegetation and estuaries by Rosentreter et al. (2023) is lower than the recent observation-based global synthesis including tidal flats and aquaculture ponds (median 1.49 (0.22–6.48) Tg CH₄ yr⁻¹) by Rosentreter et al. (2021). Total shallow coastal modern biogenic CH₄ emissions based on existing data including emissions from estuaries, coastal vegetation (Rosentreter et al., 2023), tidal flats, and human-made coastal aquaculture ponds (Rosentreter et al., 2021) amount to a median (Q1–Q3) of 1.8 (0.59–5.57) Tg CH₄ yr⁻¹. This range is about 3–4 times lower than the earlier global assessment by Borges and Abril (2011) and also lower than the value of 4–5 Tg CH₄ yr⁻¹ reported in the previous CH₄ budget for inner and outer estuaries including marshes and mangroves (Saunois et al., 2020), which was based on a significantly smaller data set (n = 80) and larger estuarine surface areas (Laruelle et al., 2013) than used here (Laruelle et al., 2025).

The nearshore (0–50 m), inner-shelf diffusive modern biogenic CH₄ flux of a median (Q1–Q3) of 1.33 (0.93–2.10) Tg CH₄ yr⁻¹ by Weber et al. (2019) based on machine-learning is similar to the combined shallow coastal (estuaries and coastal vegetation) median by Rosentreter et al. (2021, 2023). Adding the diffusive modern biogenic CH₄ flux for the outer shelf (50–200 m) (median (Q1–Q3) of 0.54 (0.40–0.73) Tg CH₄ yr⁻¹) and for the slope (200–2000 m) (median (Q1–Q3) of 0.28 (0.22–0.37) Tg CH₄ yr⁻¹) (Weber et al., 2019) and excluding geological seepage regions (Etiope et al., 2019; see below) gives a total median (Q1–Q3) of 3.95 (2.14–8.77) Tg CH₄ yr⁻¹ for combined coastal shallow, nearshore, outer shelf, and slope diffusive modern biogenic CH₄ emissions. The previous budget by Saunois et al. (2020) also included poorly constrained emissions (upper bound value: 1–2 Tg CH₄ yr⁻¹) from large river plumes protruding onto the shelves. However, here we assume that emissions from large river plumes are accounted for in the nearshore and outer shelf estimates by Weber et al. (2019). Area-integrated diffusive CH₄ emissions from the open ocean and deep seas (> 2000 m) are much lower than from other coastal systems but amount to a median (Q1–Q3) of 0.91 (0.75–1.12) Tg CH₄ yr⁻¹ because of the large surface area of the open ocean (> 300 × 10⁶ km²) (Weber et al., 2019). Overall, these marine biogenic emissions are sustained by a mixture of sedimentary production and in situ production in the sea surface layers (including the methylphosphonate pathway) (e.g. Karl et al., 2008; Repeta et al., 2016; Resplandy et al., 2024). The total coastal and ocean diffusive modern biogenic emissions retained here amount to 5 (3–10) Tg CH₄ yr⁻¹ (Table 3).

## Coastal and oceanic geological methane emissions

Submarine geological CH₄ emission is the offshore component of the general geological emissions of natural gas from the Earth's crust (Judd, 2004; Etiope, 2009; Etiope et al., 2019). The onshore components include terrestrial seeps, mud volcanoes, microseepage, and geothermal manifestations, addressed in Sect. 3.2.3. Natural gas seeping at the seabed as bubble plumes can reach the surface in relatively shallow waters (< 400 m) but CH₄-rich bubble plumes reaching the atmosphere from depths > 500 m have been observed in some cases (e.g. Solomon et al., 2009), and upwelling of bottom marine waters can, in theory, transport geological CH₄ (dissolved) to the surface from any depth. This represents, however, a small and poorly known fraction of geological CH₄ emission. Geological CH₄ can be either microbial or thermogenic, produced throughout diverse geological periods in hydrocarbon source rocks in sedimentary basins (therefore it is always fossil, ¹⁴C-free). The seepage at the seafloor is typically related to tectonic faults, sometimes forming mud diapirs and mud volcanoes (Mazzini and Etiope, 2017). Published estimates of geological CH₄ sub-sea marine emissions range from 3 to 20 Tg yr⁻¹, with a best guess of 7 Tg yr⁻¹ (Etiope and Schwietzke, 2019; Etiope et al., 2019, and references therein).

Here, the diffusive geological CH₄ emissions are estimated at 0.16 (0.11–0.24) Tg CH₄ yr⁻¹ for nearshore (0–50 m), 0.03 (0.02–0.05) Tg CH₄ yr⁻¹ for outer shelf (50–200 m), and 0.02 (0.01–0.03) Tg CH₄ yr⁻¹ for slope (200–2000 m) by calculating the fraction of the Weber et al. (2019) diffusive fluxes that occur within the identified geological seepage regions from Etiope et al. (2019). No geological seepage regions were identified in the open ocean and deep seas (> 2000 m).

In this study, we consider the ebullition flux as geologically sourced CH₄. While modern biogenic CH₄ gas production appears ubiquitous in shallow sediments (Fleischer et al., 2001; Best et al., 2006), no global data set is currently available to estimate the biogenic ebullition CH₄ flux to the atmosphere. Omission of this flux thus constitutes a significant knowledge gap in the coastal and oceanic CH₄ budget. Global geological CH₄ ebullition from continental shelf and slope, referring only to depths < 200 m, were estimated at 5.06 (1.99–8.16) Tg CH₄ yr⁻¹ (Weber et al., 2019). This estimate is based on prior estimates of the geological flux from the seafloor (Hovland et al., 1993) and bubble transfer efficiency to the ocean surface (McGinnis et al., 2006). Etiope et al. (2019) estimated a partial fraction of geological emissions in the form of gas bubbles of 3.9 (1.8–6) Tg CH₄ yr⁻¹, only referring to the sum of published estimates from 15 geological seepage regions, which are also deeper than 200 m. Global extrapolation including other 16 identified seepage zones (where flux data are not available) was suggested to be at least 7 (3–10) Tg CH₄ yr⁻¹ (Etiope et al., 2019), and this value coincides with the mean emission value (best guess).
derived by combining literature data; see Etiope and Schwietzke (2019) for further details. It is worth noting that the Weber et al. (2019) estimate of 5.06 (1.99–8.16) Tg CH₄ yr⁻¹, which considers only the continental shelf at depths < 200 m, is compatible with the overall submarine emission of 7 (3–10) Tg CH₄ yr⁻¹ (including seeps > 200 m deep) indicated in Etiope and Schwietzke (2019) and Etiope et al. (2019). Although 300–400 m is considered a general depth limit for efficient transport (with limited oxidation and dissolution) of CH₄ bubbles to the atmosphere (e.g. Judd, 2004; Schmale et al., 2005; Etiope et al., 2019), in some cases oil coatings on bubbles inhibit gas dissolution so that CH₄-rich bubbles can reach the atmosphere from depths > 500 m (e.g. Solomon et al., 2009). As mentioned above, a fraction of geological CH₄ released in deep seas (such as in the areas with gas-charged sediments inventoried in Fleischer et al., 2001) can also be transported to the surface by upwelling bottom waters. Further research is needed to better evaluate the atmospheric impact of such deep seeps.

Geological submarine emissions, thus, would amount to 0.21 (0.14–0.32) Tg CH₄ yr⁻¹ in the form of a diffusive flux, while the ebullition flux would be 5.06 (3.01–7.88) Tg CH₄ yr⁻¹, considering only < 200 m deep seepage and 7 (3–10) Tg CH₄ yr⁻¹ considering all data available (Etiope and Schwietzke, 2019). Here, we select the Etiope and Schwietzke (2019) assessment in order to account for all potential seepage areas, including those located at water depths > 200 m. While we use the Etiope and Schwietzke (2019) estimate, we acknowledge that high uncertainty remains, and other studies suggest lower ranges of emissions based on radiocarbon (¹⁴C–CH₄) data in ice cores (e.g. Hmiel et al., 2020). The suggested estimate may overestimate this source and be part of the top-down bottom-up discrepancy as discussed in Sect. 5.1.2.

As a result, here we report a (rounded) median of 12 Tg CH₄ yr⁻¹ with a range of 6–20 Tg CH₄ yr⁻¹ for all coastal and oceanic sources (Table 3).

## Methane emissions from gas hydrates

Among the different origins of coastal and oceanic CH₄, hydrates have attracted a lot of attention. CH₄ hydrates (or clathrates) are ice-like crystals formed under specific temperature and pressure conditions (Milkov, 2005). Hydrates may host either modern microbial CH₄, containing ¹⁴C and formed in situ in shallow sediments (this type of hydrates is also called “autochthonous”) or fossil, microbial, or thermogenic CH₄, migrated from deeper sediments, generally from reservoirs in hydrocarbon-rich sedimentary basins (this type of hydrates is also called “allochthonous”; Milkov, 2005; Foschi et al., 2023). The total stock of marine CH₄ hydrates is large but uncertain, with global estimates ranging from hundreds to thousands of Pg CH₄ (Klauda and Sandler, 2005; Wallmann et al., 2012). Note that the highly climate-sensitive subsea permafrost reservoir beneath Arctic Ocean shelves

also contributes to the hydrate inventory (Ruppel and Kessler, 2017).

Concerning more specifically atmospheric emissions from marine hydrates, Etiope (2015) points out that current estimates of CH₄ air–sea flux from hydrates (2–10 Tg CH₄ yr⁻¹ in Ciais et al., 2013, or Kirschke et al., 2013) originate from the hypothetical values of Cicerone and Oremland (1988). No experimental data or estimation procedures have been explicitly described along the chain of references since then (Denman et al., 2007; IPCC, 2001; Kirschke et al., 2013; Lelieveld et al., 1998). It was estimated that ~473 Tg CH₄ has been released into the water column over 100 years (Kretschmer et al., 2015). Those few teragrams per year become negligible once consumption within the water column has been accounted for. While events such as submarine slumps may trigger local releases of considerable amounts of CH₄ from hydrates that may reach the atmosphere (Etiope, 2015; Paull et al., 2002), on a global scale, present-day atmospheric CH₄ emissions from hydrates do not appear to be a significant source to the atmosphere, and at least formally, we should consider 0 (< 0.1) Tg CH₄ yr⁻¹ emissions.

## 3.2.7 Terrestrial permafrost

Permafrost is defined as frozen soil, sediment, or rock having temperatures at or below 0 °C for at least 2 consecutive years (Harris et al., 1988). The total extent of permafrost in the Northern Hemisphere is about 14 × 10⁶ km² or 15 % of the exposed land surface (Obu et al., 2019). As the climate warms, a rise in soil temperatures has been observed across the permafrost region, and permafrost thaw occurs when temperatures pass 0 °C, often associated with melting of ice in the ground (Biskaborn et al., 2019). Permafrost thaw is most pronounced in southern and spatially isolated permafrost zones, but it also occurs in northern continuous permafrost (Obu et al., 2019). Thaw occurs either as a gradual, often widespread, deepening of the active layer (surface soils that thaw every summer) or as more rapid localised thaw associated with loss of massive ground ice (thermokarst) (Turetsky et al., 2020). A total of 1000 ± 200 Pg of carbon can be found in the upper 3 m of permafrost region soils, or 1400–2000 Pg C for all permafrost (Hugelius et al., 2014; Strauss et al., 2021).

The thawing permafrost can generate direct and indirect CH₄ emissions. Direct CH₄ emissions are from the release of CH₄ contained within the thawing permafrost. This flux to the atmosphere is small and estimated to be a maximum of 1 Tg CH₄ yr⁻¹ at present (USEPA, 2010b). Increased release of CH₄ from deep geogenic sources that occurs as seepage along permafrost boundaries and lake beds may also be considered direct, and this is estimated to be 2 ± 0.4 Tg CH₄ yr⁻¹ (Walter Anthony et al., 2012). Indirect CH₄ emissions are probably more important. They are caused by (1) methanogenesis induced when the organic matter contained in thawing permafrost becomes available
for microbial decomposition; (2) thaw-induced soil wetting and changes in land surface hydrology possibly enhancing CH₄ production (McCalley et al., 2014; Schuur et al., 2022); and (3) the landscape topography changes driven by abrupt thaw processes and loss of ground ice, including the formation of thermokarst lakes, hillslope thermokarst, and wetland thermokarst (Turetsky et al., 2020). Such CH₄ production is probably already significant today and is likely to become more important in the future associated with climate change and strong positive feedback from thawing permafrost (Schuur et al., 2022). However, indirect CH₄ emissions from permafrost thawing are difficult to estimate at present, with very few data to refer to, and in any case largely overlap with wetland and freshwater emissions occurring above or around thawing areas. In a recent synthesis of full permafrost region CH₄ budgets for the period 2000–2017, Hugelius et al. (2024) compared CH₄ budgets from bottom-up and top-down (atmospheric inversion models) approaches. They estimate an integrated bottom-up budget of 50 (23, 53; mean upper and lower 95% CI) Tg CH₄ yr⁻¹, while the top-down estimate is 19 (15, 24) Tg CH₄ yr⁻¹. The bottom-up estimate is based on a combination of data-driven upscaling reported by Ramage et al. (2024) and process-based model estimates for wetland CH₄ flux calculated from model ensembles used in Saunois et al. (2020). The top-down estimate is calculated from ensembles of atmospheric inversion models used in Saunois et al. (2020). Although it is difficult with direct process-attribution, fluxes of ca. 20–30 Tg CH₄ yr⁻¹ in the bottom-up budget are caused by land cover types affected by previous permafrost thaw (thermokarst lakes, wetlands, hillslope). Because pre-thaw land cover types often have near neutral CH₄ balances (Ramage et al., 2024), these fluxes can largely be seen as driven by permafrost thaw; however the thaw may have occurred decades, or even centuries, before today.

Here, we choose to report only the direct emission range of 0–1 Tg CH₄ yr⁻¹ (Table 3), keeping in mind that current wetland, thermokarst lakes, and other freshwater methane emissions already likely include a significant indirect contribution originating from thawing permafrost.

### 3.2.8 Vegetation

Three distinct pathways for the production and emission of CH₄ by living vegetation are considered here (see Covey and Megonigal (2019) and Bastviken et al. (2023) for extensive reviews). Firstly, plants produce CH₄ through an abiotic photochemical process induced by stress (Keppler et al., 2006). This pathway was initially questioned (e.g. Dueck et al., 2007; Nisbet et al., 2009), and although numerous studies have since confirmed aerobic emissions from plants and better resolved its physical drivers (Fraser et al., 2015), global estimates still vary by 2 orders of magnitude (Liu et al., 2015). This plant source has not been confirmed in the field however, and although the potential implication for

the global CH₄ budget remains unclear, emissions from this source are certainly much smaller than originally estimated in Keppler et al. (2006) (Bloom et al., 2010; Fraser et al., 2015). Second, and of clearer significance, plant stems act as “straws”, drawing up and releasing microbially produced CH₄ from anoxic soils (Cicerone and Shetter, 1981; Rice et al., 2010; Nisbet et al., 2009). For instance, in the forested wetlands of Amazonia, tree stems are the dominant ecosystem flux pathway for soil-produced CH₄; therefore, including stem emissions in ecosystem budgets can reconcile regional bottom-up and top-down estimates (Pangala et al., 2017; Gauci et al., 2022). Third, the stems of both living trees (Covey et al., 2012) and dead wood (Covey et al., 2016) provide an environment suitable for microbial methanogenesis. Static chambers demonstrate locally significant through-bark flux from both soil-based (Pangala et al., 2013, 2015) and tree-stem-based methanogens (Pitz and Megonigal, 2017; Wang et al., 2016). A synthesis indicates stem CH₄ emissions significantly increase the source strength of forested wetlands and modestly decrease the sink strength of upland forests (Covey and Megonigal, 2019). Recently, fieldwork suggested that trees may also act as a CH₄ sink (Machacova et al., 2021; Gorgolewski et al., 2023; Gauci et al., 2024). The scientific activity covering CH₄ emissions in forested ecosystems reveals a far more complex story than previously thought, with an interplay of productive/consumptive, aerobic/anaerobic, and biotic/abiotic processes occurring between upland/wetland soils, trees, and atmosphere. Understanding the complex processes that regulate CH₄ source-sink dynamics in forests and estimating their contribution to the global CH₄ budget requires cross-disciplinary research, more observations, and new models that can overcome the classical binary classifications of wetland versus upland forest and of emitting versus uptaking soils (Barba et al., 2019; Covey and Megonigal, 2019). Although we recognise these emissions are potentially large (particularly tree transport from inundated soil), global estimates for each of these pathways remain highly uncertain and/or are currently included here within other flux category sources (e.g. inland waters, wetlands, upland soils).

### 3.3 Methane sinks and lifetime

CH₄ is the most abundant reactive trace gas in the troposphere, and its reactivity is important to both tropospheric and stratospheric chemistry. The main atmospheric sink of CH₄ (~90% of the total sink mechanism) is oxidation by the hydroxyl radical (OH), mostly in the troposphere (Ehhalt, 1974). Other losses are by photochemistry in the stratosphere (reactions with chlorine atoms (Cl) and excited atomic oxygen (O¹D)), oxidation in soils (Curry, 2007; Dutaur and Verchot, 2007), and by photochemistry in the marine boundary layer (reaction with Cl; Allan et al. (2007), Thornton et al. (2010)). Uncertainties in the total sink of CH₄ as estimated by atmospheric chemistry models are of the or-
der of 20%–40% (Saunois et al., 2016). It is much less (10%–20%) when using atmospheric proxy methods (e.g. methyl chloroform; see below) as in atmospheric inversions (Saunois et al., 2016). In the present release of the global CH₄ budget, we estimate bottom-up CH₄ chemical sinks and lifetime mainly based on global model results from the Chemistry Climate Model Initiative (CCMI) 2022 activity (Plummer et al., 2021) and CMIP6 simulations (Collins et al., 2017).

### 3.3.1 Tropospheric OH oxidation

OH radicals are produced following the photolysis of ozone (O₃) in the presence of water vapour. OH is destroyed by reactions with carbon monoxide (CO), CH₄, and non-methane volatile organic compounds.

Following the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP), which studied the long-term changes in atmospheric composition between 1850 and 2100 (Lamarque et al., 2013), a new series of experiments was conducted by several chemistry–climate models and chemistry–transport models participating in the Chemistry–Climate Model Initiative (CCMI) (Plummer et al., 2021). Mass-weighted OH tropospheric concentrations do not directly represent CH₄ loss, as the spatial and vertical distributions of OH affect this loss through, in particular, the temperature dependency and the distribution of CH₄ (e.g. Zhao et al., 2019). However, estimating OH concentrations and spatial and vertical distributions is a key step in estimating methane loss through OH. Over the period 2000–2010, the global mass-weighted OH tropospheric concentration is estimated at 13.3 [11.7–18.2] × 10⁵ molecules cm⁻³ by eight CCMI-2022 models and at 11.8 [9.4–13.5] × 10⁵ molecules cm⁻³ by nine models contributing CMIP6 historical run (Collins et al., 2017) (see Table S4). The ranges calculated here are similar to the ones proposed previously in Saunois et al. (2020), where the multi-model mean (11 models) global mass-weighted OH tropospheric concentration was 11.7 ± 1.0 × 10⁵ molecules cm⁻³ (range 9.9–14.4 × 10⁵ molecules cm⁻³; Zhao et al. (2019)) consistent with the previous estimates from ACCMIP (11.7 ± 1.0 × 10⁵ molecules cm⁻³, with a range of 10.3–13.4 × 10⁵ molecules cm⁻³; Voulgarakis et al. (2013) for year 2000) and the estimates of Prather et al. (2012) of 11.2 ± 1.3 × 10⁵ molecules cm⁻³. Nicely et al. (2017) attribute the differences in OH simulated by different chemistry–transport models to, in decreasing order of importance, different chemical mechanisms, various treatments of the photolysis rate of O₃, and modelled O₃ and CO. Besides the uncertainty on global OH concentrations, there is an uncertainty in the spatial and temporal distribution of OH. Models often simulate higher OH in the Northern Hemisphere (NH) than in the Southern Hemisphere (SH), leading to a NH/SH OH ratio greater than 1 (e.g. Zhao et

al., 2019). However, there is evidence for parity in interhemispheric OH concentrations (Patra et al., 2014), which needs to be confirmed by other observational and model-derived estimates. The analyses of the latest CCMI (Plummer et al., 2021) and CMIP6 (Collins et al., 2017) model outputs show that structural uncertainties in the atmospheric chemistry models remain large, probably due to inherent biases in OH precursors. Such biases have been highlighted in the OH 3D fields simulated by two atmospheric chemistry models (Zhao et al., 2023) and were corrected using OH precursor observations. Such corrections resulted in tropospheric OH mean concentrations lowered by 2 × 10⁵ molecules cm⁻³, leading to around 10 × 10⁵ molecules cm⁻³, and a NH/SH OH ratio closer to 1, in better agreement with methyl chloroform (MCF)-based approaches. This study highlights the need for further improvement of the atmospheric chemistry model.

OH concentrations and their changes can be sensitive to climate variability (e.g. Nicely et al., 2018; Anderson et al., 2021), biomass burning (e.g. Anderson et al., 2024), and anthropogenic emissions of precursors (Peng et al., 2022; Stevenson et al., 2020). OH distributions calculated by chemistry–climate models show large regional differences and various vertical profiles (Zhao et al., 2019). OH changes present also regional differences in the long term (Stevenson et al., 2020). Despite large regional changes, the global mean OH concentration was suggested to have changed only slightly from 1850 to 1980 but followed by strong (9%) increases up to the present day (Stevenson et al., 2020). This increase simulated by models over 2000–2015 is however not in agreement with observation-based approaches (Thompson et al., 2024; Patra et al., 2021; Nicely et al., 2018; Rigby et al., 2017; Turner et al., 2018) where OH decreases or remains constant over the period. CCMI and CMIP6 models show OH interannual variability ranging from 0.9% to 1.8% over 2000–2010 (Table S4), in agreement with the values of interannual variability (IAV) derived from some observationally constrained studies (e.g. Thompson et al., 2024; Montzka et al., 2011) but lower than values deduced from methyl chloroform measurements (Patra et al., 2021; Naus et al., 2021). However, chemistry–climate simulations consider meteorology variability but not fully emission interannual variability (e.g. from biomass burning) and thus are expected to simulate lower OH interannual variability than in reality. Using an empirical model constrained by global observations of O₃, water vapour, CH₄, and temperature as well as the simulated effects of changing NOₓ emissions and tropical expansion, Nicely et al. (2017) found an interannual variability in OH of about 1.3%–1.6% between 1980 and 2015, in agreement with methyl-chloroform-based estimates (Montzka et al., 2011).

Over 2000–2009, the tropospheric loss (tropopause height at 200 hPa) of CH₄ by OH oxidation derived from the 10 simulations contributing to CMIP6 modelling activity (see Table S5) is estimated at 546 [446–663] Tg CH₄ yr⁻¹ (Ta-