ESSD - The Global Methane Budget 2000–2017

ESSD - The Global Methane Budget 2000–2017
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the Creative Commons Attribution 4.0 License.
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Review article
15 Jul 2020
Review article |
15 Jul 2020
The Global Methane Budget 2000–2017
The Global Methane Budget 2000–2017
The Global Methane Budget 2000–2017
Marielle Saunois et al.
Marielle Saunois
Ann R. Stavert
Ben Poulter
Philippe Bousquet
Josep G. Canadell
Robert B. Jackson
Peter A. Raymond
Edward J. Dlugokencky
Sander Houweling
Prabir K. Patra
Philippe Ciais
Vivek K. Arora
David Bastviken
Peter Bergamaschi
Donald R. Blake
Gordon Brailsford
Lori Bruhwiler
Kimberly M. Carlson
Mark Carrol
Simona Castaldi
Naveen Chandra
Cyril Crevoisier
Patrick M. Crill
Kristofer Covey
Charles L. Curry
Giuseppe Etiope
Christian Frankenberg
Nicola Gedney
Michaela I. Hegglin
Lena Höglund-Isaksson
Gustaf Hugelius
Misa Ishizawa
Akihiko Ito
Greet Janssens-Maenhout
Katherine M. Jensen
Fortunat Joos
Thomas Kleinen
Paul B. Krummel
Ray L. Langenfelds
Goulven G. Laruelle
Licheng Liu
Toshinobu Machida
Shamil Maksyutov
Kyle C. McDonald
Joe McNorton
Paul A. Miller
Joe R. Melton
Isamu Morino
Jurek Müller
Fabiola Murguia-Flores
Vaishali Naik
Yosuke Niwa
Sergio Noce
Simon O'Doherty
Robert J. Parker
Changhui Peng
Shushi Peng
Glen P. Peters
Catherine Prigent
Ronald Prinn
Michel Ramonet
Pierre Regnier
William J. Riley
Judith A. Rosentreter
Arjo Segers
Isobel J. Simpson
Hao Shi
Steven J. Smith
L. Paul Steele
Brett F. Thornton
Hanqin Tian
Yasunori Tohjima
Francesco N. Tubiello
Aki Tsuruta
Nicolas Viovy
Apostolos Voulgarakis
Thomas S. Weber
Michiel van Weele
Guido R. van der Werf
Ray F. Weiss
Doug Worthy
Debra Wunch
Yi Yin
Yukio Yoshida
Wenxin Zhang
Zhen Zhang
Yuanhong Zhao
Bo Zheng
Qing Zhu
Qiuan Zhu
and
Qianlai Zhuang
Marielle Saunois
CORRESPONDING AUTHOR
marielle.saunois@lsce.ipsl.fr
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Ann R. Stavert
Global Carbon Project, CSIRO Oceans and Atmosphere, Aspendale, VIC
3195 & Canberra, ACT 2601, Australia
Ben Poulter
NASA Goddard Space Flight Center, Biospheric Science Laboratory,
Greenbelt, MD 20771, USA
Philippe Bousquet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Josep G. Canadell
Global Carbon Project, CSIRO Oceans and Atmosphere, Aspendale, VIC
3195 & Canberra, ACT 2601, Australia
Robert B. Jackson
Department of Earth System Science, Woods Institute for the
Environment, and Precourt Institute for Energy, Stanford University,
Stanford, CA 94305-2210, USA
Peter A. Raymond
Yale School of the Environment, Yale University, New
Haven, CT 06511, USA
Edward J. Dlugokencky
NOAA Global Monitoring Laboratory, 325 Broadway, Boulder, CO 80305, USA
Sander Houweling
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584 CA
Utrecht, the Netherlands
Vrije Universiteit Amsterdam, Department of Earth Sciences, Earth and
Climate Cluster, VU Amsterdam, Amsterdam, the Netherlands
Prabir K. Patra
Research Institute for Global Change, JAMSTEC, 3173-25 Showa-machi,
Kanazawa, Yokohama, 236-0001, Japan
Center for Environmental Remote Sensing, Chiba University, Chiba,
Japan
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Vivek K. Arora
Canadian Centre for Climate Modelling and Analysis, Climate Research
Division, Environment and Climate Change Canada, Victoria, BC, V8W 2Y2,
Canada
David Bastviken
Department of Thematic Studies – Environmental Change, Linköping
University, 581 83 Linköping, Sweden
Peter Bergamaschi
European Commission Joint Research Centre, Via E. Fermi 2749, 21027
Ispra (Va), Italy
Donald R. Blake
Department of Chemistry, University of California Irvine, 570 Rowland
Hall, Irvine, CA 92697, USA
Gordon Brailsford
National Institute of Water and Atmospheric Research, 301 Evans Bay
Parade, Wellington, New Zealand
Lori Bruhwiler
NOAA Global Monitoring Laboratory, 325 Broadway, Boulder, CO 80305, USA
Kimberly M. Carlson
Department of Environmental Studies, New York University, New York, NY 10003, USA
Department of Natural Resources and Environmental Management,
University of Hawai'i, Honolulu, HI 96822, USA
Mark Carrol
NASA Goddard Space Flight Center, Computational and Information Science and Technology Office, Greenbelt, MD 20771, USA
Simona Castaldi
Dipartimento di Scienze Ambientali, Biologiche e Farmaceutiche,
Università degli Studi della Campania Luigi Vanvitelli, via Vivaldi 43,
81100 Caserta, Italy
Department of Landscape Design and Sustainable Ecosystems, RUDN
University, Moscow, Russia
Impacts on Agriculture, Forests, and Ecosystem Services Division,
Centro Euro-Mediterraneo sui Cambiamenti Climatici, Via Augusto Imperatore
16, 73100 Lecce, Italy
Naveen Chandra
Research Institute for Global Change, JAMSTEC, 3173-25 Showa-machi,
Kanazawa, Yokohama, 236-0001, Japan
Cyril Crevoisier
Laboratoire de Météorologie Dynamique, LMD-IPSL, Ecole
Polytechnique, 91120 Palaiseau, France
Patrick M. Crill
Department of Geological Sciences and Bolin Centre for Climate
Research, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
Kristofer Covey
Environmental Studies and Sciences Program, Skidmore College,
Saratoga Springs, NY 12866, USA
Charles L. Curry
Pacific Climate Impacts Consortium,
University of Victoria, University House 1, P.O. Box 1700 STN CSC
Victoria, BC V8W 2Y2, Canada
School of Earth and Ocean Sciences, University of Victoria, P.O. Box 1700 STN CSC, Victoria, V8W 2Y2 BC, Canada
Giuseppe Etiope
Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma 2, via
V. Murata 605 00143 Rome, Italy
Faculty of Environmental Science and Engineering, Babes Bolyai
University, Cluj-Napoca, Romania
Christian Frankenberg
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Nicola Gedney
Met Office Hadley Centre, Joint Centre for Hydrometeorological
Research, Maclean Building, Wallingford OX10 8BB, UK
Michaela I. Hegglin
Department of Meteorology, University of Reading, Earley Gate,
Reading RG6 6BB, UK
Lena Höglund-Isaksson
Air Quality and Greenhouse Gases Program (AIR), International
Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria
Gustaf Hugelius
Department of Physical Geography and Bolin Centre for Climate
Research, Stockholm University, 106 91 Stockholm, Sweden
Misa Ishizawa
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Akihiko Ito
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Greet Janssens-Maenhout
European Commission Joint Research Centre, Via E. Fermi 2749, 21027
Ispra (Va), Italy
Katherine M. Jensen
Department of Earth and Atmospheric Sciences, City College of New
York, City University of New York, New York, NY 10031, USA
Fortunat Joos
Climate and Environmental Physics, Physics Institute and Oeschger
Centre for Climate Change Research, University of Bern, Sidlerstr. 5, 3012
Bern, Switzerland
Thomas Kleinen
Max Planck Institute for Meteorology, Bundesstr. 53, 20146
Hamburg, Germany
Paul B. Krummel
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale,
Victoria 3195, Australia
Ray L. Langenfelds
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale,
Victoria 3195, Australia
Goulven G. Laruelle
Department Geoscience, Environment & Society, Université Libre
de Bruxelles, 1050-Brussels, Belgium
Licheng Liu
Department of Earth, Atmospheric, Planetary Sciences, Department of
Agronomy, Purdue University, West Lafayette, IN 47907, USA
Toshinobu Machida
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Shamil Maksyutov
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Kyle C. McDonald
Department of Earth and Atmospheric Sciences, City College of New
York, City University of New York, New York, NY 10031, USA
Joe McNorton
Research Department, European Centre for Medium-Range Weather
Forecasts, Reading, UK
Paul A. Miller
Department of Physical Geography and Ecosystem Science, Lund
University, Sölvegatan 12, 223 62, Lund, Sweden
Joe R. Melton
Climate Research Division, Environment and Climate Change Canada,
Victoria, BC, V8W 2Y2, Canada
Isamu Morino
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Jurek Müller
Climate and Environmental Physics, Physics Institute and Oeschger
Centre for Climate Change Research, University of Bern, Sidlerstr. 5, 3012
Bern, Switzerland
Fabiola Murguia-Flores
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Vaishali Naik
NOAA/Geophysical Fluid Dynamics Laboratory (GFDL), 201 Forrestal Rd.,
Princeton, NJ 08540, USA
Yosuke Niwa
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Meteorological Research Institute (MRI), Nagamine 1-1, Tsukuba,
Ibaraki 305-0052, Japan
Sergio Noce
Impacts on Agriculture, Forests, and Ecosystem Services Division,
Centro Euro-Mediterraneo sui Cambiamenti Climatici, Via Augusto Imperatore
16, 73100 Lecce, Italy
Simon O'Doherty
School of Chemistry, University of Bristol, Cantock's Close, Clifton,
Bristol BS8 1TS, UK
Robert J. Parker
National Centre for Earth Observation, University of Leicester,
Leicester, LE1 7RH, UK
Changhui Peng
Department of Biology Sciences, Institute of Environment Science,
University of Quebec at Montreal, Montreal, QC H3C 3P8, Canada
Shushi Peng
Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Glen P. Peters
CICERO Center for International Climate Research, Pb. 1129 Blindern, 0318 Oslo, Norway
Catherine Prigent
Observatoire de Paris, Université PSL, Sorbonne Université, CNRS, LERMA, Paris, France
Ronald Prinn
Department of Earth, Atmospheric and Planetary Sciences,
Massachusetts Institute of Technology (MIT), Building 54-1312, Cambridge, MA
02139, USA
Michel Ramonet
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Pierre Regnier
Department Geoscience, Environment & Society, Université Libre
de Bruxelles, 1050-Brussels, Belgium
William J. Riley
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Lab, 1 Cyclotron Road, Berkeley, CA 94720, USA
Judith A. Rosentreter
Centre for Coastal Biogeochemistry, School of Environment, Science
and Engineering, Southern Cross University, Lismore, NSW 2480, Australia
Arjo Segers
TNO, Dep. of Climate Air & Sustainability, P.O. Box 80015,
NL-3508-TA, Utrecht, the Netherlands
Isobel J. Simpson
Department of Chemistry, University of California Irvine, 570 Rowland
Hall, Irvine, CA 92697, USA
Hao Shi
International Center for Climate and Global Change Research, School
of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive,
Auburn, AL 36849, USA
Steven J. Smith
Joint Global Change Research Institute, Pacific Northwest National
Lab, College Park, MD 20740, USA
Department of Atmospheric and Oceanic Science, University of
Maryland, College Park, MD 20740, USA
L. Paul Steele
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale,
Victoria 3195, Australia
Brett F. Thornton
Department of Geological Sciences and Bolin Centre for Climate
Research, Stockholm University, Svante Arrhenius väg 8, 106 91 Stockholm, Sweden
Hanqin Tian
International Center for Climate and Global Change Research, School
of Forestry and Wildlife Sciences, Auburn University, 602 Duncan Drive,
Auburn, AL 36849, USA
Yasunori Tohjima
Center for Environmental Measurement and Analysis, National Institute for Environmental Studies (NIES), Onogawa16-2, Tsukuba, Ibaraki 305-8506, Japan
Francesco N. Tubiello
Statistics Division, Food and Agriculture Organization of the United
Nations (FAO), Viale delle Terme di Caracalla, 00153 Rome, Italy
Aki Tsuruta
Finnish Meteorological Institute, P.O. Box 503, 00101, Helsinki,
Finland
Nicolas Viovy
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Apostolos Voulgarakis
Department of Physics, Imperial College London, London SW7 2AZ, UK
School of Environmental Engineering, Technical University of Crete,
Chania, Greece
Thomas S. Weber
Department of Earth and Environmental Sciences, University of
Rochester, Rochester, NY 14627, USA
Michiel van Weele
KNMI, P.O. Box 201, 3730 AE, De Bilt, the Netherlands
Guido R. van der Werf
Vrije Universiteit Amsterdam, Department of Earth Sciences, Earth and
Climate Cluster, VU Amsterdam, Amsterdam, the Netherlands
Ray F. Weiss
Scripps Institution of Oceanography (SIO), University of California
San Diego, La Jolla, CA 92093, USA
Doug Worthy
Environment and Climate Change Canada, 4905, rue Dufferin, Toronto, Canada
Debra Wunch
Department of Physics, University of Toronto, 60 St. George Street,
Toronto, Ontario, Canada
Yi Yin
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Yukio Yoshida
Center for Global Environmental Research, National Institute for
Environmental Studies (NIES), Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan
Wenxin Zhang
Department of Physical Geography and Ecosystem Science, Lund
University, Sölvegatan 12, 223 62, Lund, Sweden
Zhen Zhang
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
Yuanhong Zhao
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Bo Zheng
Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL
(CEA-CNRS-UVSQ), Université Paris-Saclay 91191 Gif-sur-Yvette, France
Qing Zhu
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Lab, 1 Cyclotron Road, Berkeley, CA 94720, USA
Qiuan Zhu
College of Hydrology and Water Resources, Hohai University, Nanjing,
210098, China
Qianlai Zhuang
Department of Earth, Atmospheric, Planetary Sciences, Department of
Agronomy, Purdue University, West Lafayette, IN 47907, USA
Abstract
Understanding and quantifying the global methane (
CH
) budget
is important for assessing realistic pathways to mitigate climate change.
Atmospheric emissions and concentrations of
CH
continue to increase,
making
CH
the second most important human-influenced greenhouse gas in
terms of climate forcing, after carbon dioxide (
CO
). The relative
importance of
CH
compared to
CO
depends on its shorter
atmospheric lifetime, stronger warming potential, and variations in
atmospheric growth rate over the past decade, the causes of which are still
debated. Two major challenges in reducing uncertainties in the atmospheric
growth rate arise from the variety of geographically overlapping
CH
sources and from the destruction of
CH
by short-lived hydroxyl
radicals (OH). To address these challenges, we have established a
consortium of multidisciplinary scientists under the umbrella of the Global
Carbon Project to synthesize and stimulate new research aimed at improving
and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper
dedicated to the decadal methane budget, integrating results of top-down
studies (atmospheric observations within an atmospheric inverse-modelling
framework) and bottom-up estimates (including process-based models for
estimating land surface emissions and atmospheric chemistry, inventories of
anthropogenic emissions, and data-driven extrapolations).
For the 2008–2017 decade, global methane emissions are estimated by
atmospheric inversions (a top-down approach) to be 576 Tg
CH
yr
−1
(range 550–594, corresponding to the minimum and maximum
estimates of the model ensemble). Of this total, 359 Tg
CH
yr
−1
or
60 % is attributed to anthropogenic sources, that is
emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg
CH
yr
−1
or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is
29 Tg
CH
yr
−1
larger than our estimate for the previous decade (2000–2009),
and 24 Tg
CH
yr
−1
larger than the one reported in the previous
budget for 2003–2012 (Saunois et al., 2016). Since 2012, global
CH
emissions have been tracking the warmest scenarios assessed by the
Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost
30 % larger global emissions (737 Tg
CH
yr
−1
, range 594–881)
than top-down inversion methods. Indeed, bottom-up estimates for natural
sources such as natural wetlands, other inland water systems, and geological
sources are higher than top-down estimates. The atmospheric constraints on
the top-down budget suggest that at least some of these bottom-up emissions
are overestimated. The latitudinal distribution of atmospheric
observation-based emissions indicates a predominance of tropical emissions
65 % of the global budget,
30
N)
compared to mid-latitudes (
30 %, 30–60
N)
and high northern latitudes (
4 %, 60–90
N). The most important source of uncertainty in the methane
budget is attributable to natural emissions, especially those from wetlands
and other inland waters.
Some of our global source estimates are smaller than those in previously
published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg
CH
yr
−1
lower due to
improved partition wetlands and other inland waters. Emissions from
geological sources and wild animals are also found to be smaller by 7 Tg
CH
yr
−1
by 8 Tg
CH
yr
−1
, respectively. However, the overall
discrepancy between bottom-up and top-down estimates has been reduced by
only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane
budget include (i) a global, high-resolution map of water-saturated soils
and inundated areas emitting methane based on a robust classification of
different types of emitting habitats; (ii) further development of
process-based models for inland-water emissions; (iii) intensification of
methane observations at local scales (e.g., FLUXNET-
CH
measurements)
and urban-scale monitoring to constrain bottom-up land surface models, and
at regional scales (surface networks and satellites) to constrain
atmospheric inversions; (iv) improvements of transport models and the
representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or
co-emitted species such as ethane to improve source partitioning.
The data presented here can be downloaded from
(Saunois et al., 2020) and from the
Global Carbon Project.
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Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020.
Received: 22 Jul 2019
Discussion started: 19 Aug 2019
Revised: 20 May 2020
Accepted: 29 May 2020
Published: 15 Jul 2020
Introduction
The surface dry air mole fraction of atmospheric methane (
CH
) reached
1857 ppb in 2018 (Fig. 1), approximately 2.6 times greater than its
estimated pre-industrial equilibrium 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 methane fluxes due to increased atmospheric
CO
concentrations and climate change
(Ciais et al., 2013). Atmospheric
CH
is a stronger absorber of Earth's emitted thermal infrared
radiation than carbon dioxide (
CO
), as assessed by its global warming
potential (GWP) relative to
CO
. For a 100-year time horizon and without
considering climate feedbacks GWP(
CH
28
(IPCC AR5; Myhre et al., 2013).
Although global anthropogenic emissions of
CH
are estimated at around
366 Tg
CH
yr
−1
(Saunois
et al., 2016), representing only 3 % of the global
CO
anthropogenic
emissions in units of carbon mass flux, the increase in atmospheric
CH
concentrations has contributed
23 % (
0.62 W m
−2
) to the additional radiative forcing accumulated in the lower
atmosphere since 1750 (Etminan et al.,
2016). Changes in other chemical compounds (such as nitrogen oxides,
NO
, or carbon monoxide, CO) also influence the forcing of
atmospheric
CH
through changes to its atmospheric lifetime. From an
emission perspective, the total radiative forcing attributable to
anthropogenic
CH
emissions is currently about 0.97 W m
−2
(Myhre et al., 2013).
Emissions of
CH
contribute to the production of ozone, stratospheric
water vapour, and
CO
and most importantly affect its own lifetime
(Myhre
et al., 2013; Shindell et al., 2012).
CH
has a short lifetime in the
atmosphere (about 9 years for the year 2010; Prather et al., 2012); hence a stabilization
or reduction of
CH
emissions leads rapidly, in a few decades, to a
stabilization or reduction of its atmospheric concentration and therefore
its radiative forcing. Reducing
CH
emissions is therefore recognized
as an effective option for rapid climate change mitigation, especially on
decadal timescales (Shindell et al., 2012),
because of its shorter lifetime than
CO
Figure 1
Globally averaged atmospheric
CH
(ppb)
(a)
and its annual
growth rate
ATM
(ppb yr
−1
(b)
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).
Of concern, the current anthropogenic methane emissions trajectory is
estimated to lie between the two warmest IPCC-AR5 scenarios
(Nisbet et al., 2016, 2019), i.e., RCP8.5 and RCP6.0, corresponding to
temperature increases above 3
C by the end of this century. This
trajectory implies that large reductions of methane emissions are needed to
meet the 1.5–2
C target of the Paris Agreement
(Collins
et al., 2013; Nisbet et al., 2019). Moreover,
CH
is a precursor of
important air pollutants such as ozone, and, as such, its emissions are
covered by two international conventions: the United Nations Framework
Convention on Climate Change (UNFCCC) and the Convention on Long-Range
Transboundary Air Pollution (CLRTAP), another motivation to reduce its
emissions.
Changes in the magnitude and temporal variation (annual to inter-annual) in
methane sources and sinks over the past decades are characterized by large
uncertainties
(Kirschke et al., 2013; Saunois et al., 2017; Turner et al., 2019). Also, the decadal
budget suggests relative uncertainties (hereafter reported as min–max
ranges) of 20 %–35 % for inventories of anthropogenic emissions in specific
sectors (e.g., agriculture, waste, fossil fuels), 50 % for biomass burning
and natural wetland emissions, and reaching 100 % or more for other
natural sources (e.g. inland waters, geological sources).
The uncertainty in
the chemical loss of methane by OH, the predominant sink of atmospheric
methane, is estimated around 10 % (Prather
et al., 2012) to 15 % (from bottom-up approaches in Saunois et al., 2016).
This represents, for the top-down methods, the minimum relative uncertainty
associated with global methane emissions, as other methane 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
CH
emissions to total emissions can be
quantified by combining lifetime estimates with reconstructed pre-industrial
atmospheric methane concentrations from ice cores
(e.g. Ehhalt et al., 2001).
Regionally, uncertainties in emissions may reach 40 %–60 % (e.g. for South
America, Africa, China, and India; see Saunois et al., 2016).
In order to verify future emission reductions, for example to help conduct
Paris Agreement's stocktake, sustained and long-term monitoring of the
methane cycle is needed to reach more precise estimation of trends, and
reduced uncertainties in anthropogenic emissions (Bergamaschi et al., 2018a;
Pacala, 2010). Reducing uncertainties in individual methane sources and
thus in the overall methane budget is challenging for at least four reasons.
Firstly, methane is emitted by a variety of processes, including both
natural and anthropogenic sources, point and diffuse sources, and sources
associated with three different emission classes (i.e., biogenic,
thermogenic, and pyrogenic). These multiple sources and processes require the
integration of data from diverse scientific communities. The fact that
anthropogenic emissions result from unintentional leakage from fossil fuel
production or agriculture further complicates production of accurate
bottom-up emission estimates. Secondly, atmospheric methane is removed by
chemical reactions in the atmosphere involving radicals (mainly OH) that
have very short lifetimes (typically
1 s). The spatial and
temporal distributions of OH are highly variable. Although OH can be
measured locally, calculating global
CH
loss through OH measurements
would require high-resolution OH measurements (typically half an hour to
integrate cloud cover and 1 km spatially to consider OH high reactivity and
heterogeneity). As a result, such a calculation is currently possible only
through modelling. However, simulated OH concentrations from chemistry–climate models still show uncertain spatio-temporal distribution at regional
to global scales
(Zhao et al.,
2019). Thirdly, only the net methane budget (sources minus sinks) is
constrained by precise observations of atmospheric growth rates
(Dlugokencky et al., 2009), leaving the
sum of sources and the sum of sinks more uncertain. One simplification for
CH
compared to
CO
is that the oceanic contribution to the global
methane budget is small (
1 %–3 %), making source estimation
predominantly a continental problem (USEPA, 2010b).
Finally, we lack observations to constrain (1) process models that produce
estimates of wetland extent (Kleinen et al., 2012;
Stocker et al., 2014) and wetland emissions (Melton
et al., 2013; Poulter et al., 2017; Wania et al., 2013), (2) other inland
water sources (Bastviken et al., 2011; Wik
et al., 2016a), (3) inventories of anthropogenic emissions
(Höglund-Isaksson, 2012,
2017; Janssens-Maenhout et al., 2019; USEPA, 2012), and (4) atmospheric
inversions, which aim to estimate methane emissions from global to regional
scales
(Bergamaschi
et al., 2013, 2018b; Houweling et al., 2014; Kirschke et
al., 2013; Saunois et al., 2016; Spahni et al., 2011; Thompson et al., 2017;
Tian et al., 2016).
The global methane budget inferred from atmospheric observations by
atmospheric inversions relies on regional constraints from atmospheric
sampling networks, which are relatively dense for northern mid-latitudes,
with a number of high-precision and high-accuracy surface stations, but are
sparser at tropical latitudes and in the Southern Hemisphere
(Dlugokencky
et al., 2011). Recently the atmospheric observation density has increased
in the tropics due to satellite-based platforms that provide column-average
methane mixing ratios. Despite continuous improvements in the precision and
accuracy of space-based measurements
(e.g. Buchwitz et al., 2017),
systematic errors greater than several parts per billion on total column observations can
still limit the usage of such data to constrain surface emissions (Alexe
et al., 2015; Bousquet et al., 2018; Chevallier et al., 2017; Locatelli et
al., 2015). The development of robust bias corrections on existing data can
help overcome this issue
(e.g.
Inoue et al., 2016) and satellite-based inversions have been suggested to
reduce global and regional flux uncertainties compared to surface-based
inversions (e.g. Fraser et al.,
2013).
The Global Carbon Project (GCP) seeks to develop a complete picture of the
carbon cycle by establishing common, consistent scientific knowledge to
support policy
debate and actions to mitigate greenhouse gas emissions to
the atmosphere (
, last access: 24 June 2020). The objective of this paper is
to analyse and synthesize the current knowledge of the global methane
budget, by gathering results of observations and models in order to better
understand and quantify the main robust features of this budget and its
remaining uncertainties and to make recommendations. We combine results
from a large ensemble 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 methane 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 on decadal budgets and
on the update of the previous assessment made for the period 2003–2012 to
the more recent 2008–2017 decade. More in-depth analysis of trends and
year-to-year changes is left to future publications. The regional budget is
further discussed in Stavert et al. (2020) and synthetised in Jackson et al., 2020. Our current paper is a living review, published at about 3-year
intervals, to provide an update and new synthesis of available
observational, statistical, and model data for the overall
CH
budget
and its individual components.
Kirschke et al. (2013) were the first to conduct a
CH
budget synthesis and were
followed by Saunois et al. (2016). Kirschke et al. (2013) reported decadal
mean
CH
emissions and sinks from 1980 to 2009 based on bottom-up and
top-down approaches. Saunois et al. (2016) reported methane emissions for
three time periods: (1) the last calendar decade (2000–2009), (2) the last
available decade (2003–2012), and (3) the last available year (2012) at the
time. Here, we update reporting methane emissions and sinks for 2000–2009
decade, for the most recent 2008–2017 decade where data are available, and
for the year 2017, reducing the time lag between the last reported year and
analysis. The methane budget is presented here at global and latitudinal
scales, and data can be downloaded from
(Saunois et al., 2019).
Five sections follow this introduction. Section 2 presents the methodology
used in the budget (units, definitions of source categories and regions,
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
methane 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 methane budget by comparing bottom-up and
top-down estimates and highlighting differences. Finally, Sect. 6
discusses future developments, missing components, and the most critical
remaining uncertainties based on our update to the global methane budget.
Methodology
2.1
Units used
Unless specified, fluxes are expressed in teragrams of
CH
per year
(1 Tg
CH
yr
−1
10
12
CH
yr
−1
), while atmospheric
concentrations are expressed as dry air mole fractions, in parts per billion
(ppb), with atmospheric methane annual increases,
ATM
, expressed in
parts per billion per year. In the tables, we present mean values and ranges for the two
decades 2000–2009 and 2008–2017, together with results for the most recent
available year (2017). Results obtained from previous syntheses (i.e.
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, 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
CH
emission estimates are provided with up to three 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
methane budget, we encourage the reader to consider not more than two digits
as significant.
2.2
Period of the budget and availability of data
The bottom-up estimates rely on global anthropogenic inventories,
land surface models for wetland emissions, and published literature for other
natural sources. The global gridded anthropogenic inventories are updated
irregularly, generally every 3 to 5 years. The last reported years of
available inventories were 2012, 2014, or 2016 when we started this study.
For this budget, in order to cover the reported period (2000–2017), it was
necessary to extrapolate some of these datasets as explained in Sect. 3.1.1.
The surface land models were run over the full period 2000–2017 using
dynamical wetland areas (Sect. 3.2.1).
For the top-down estimates, we use atmospheric inversions covering
2000–2017. The simulations run until mid-2018, but the last year of reported
inversion results is 2017, which represents a 3-year lag with
the present, a 2-year-shorter lag than for the last release (Saunois et
al., 2016). Satellite observations are linked to operational data chains and
are generally available days to weeks after the recording of the spectra.
Surface observations can lag from months to years because
of the time for
flask analyses and data checks in (mostly) non-operational chains. The final
6 months of inversions are generally ignored (spin down) because the
estimated fluxes are not constrained by as many observations as the previous
periods.
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. (2020), a set of 19
regions (oceans and 18 continental regions; see Fig. S1 in the Supplement) were
used. As anthropogenic emissions are often reported by country, we define
these regions based on a country list (Table S1). 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
inter-comparison map
(Gurney et al., 2004)
but with subdivisions to separate the contribution from important countries
or regions for the methane cycle (China, South Asia, tropical America,
tropical Africa, the United States, and Russia). The resulting region definition is the
same as used for the GCP
budget (Tian et al., 2019).
2.4
Definition of source categories
Methane is emitted by different processes (i.e., biogenic, thermogenic, or
pyrogenic) and can be of anthropogenic or natural origin. Biogenic methane
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 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 methane
reaches the atmosphere through marine and land geological gas seeps. These
methane emissions are increased by human activities, for instance the
exploitation and distribution of fossil fuels. Pyrogenic methane is produced
by the incomplete combustion of biomass and other organic material. Peat
fires, biomass burning in deforested or degraded areas, wildfires, and
biofuel burning are the largest sources of pyrogenic methane. Methane
hydrates, ice-like cages of trapped methane found in continental shelves and
slopes and below sub-sea 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 methane sources depending on
their anthropogenic or natural origin, which is relevant for climate policy.
Here, “natural sources” refer to pre-agricultural emissions even if they
are perturbed by anthropogenic climate change, and “anthropogenic sources”
are caused by direct human activities since pre-industrial/pre-agricultural
time (3000–2000 BCE; Nakazawa et al., 1993) including
agriculture, waste management, and fossil-fuel-related activities. Natural
emissions are split between “wetland” and “other natural” emissions
(e.g., non-wetland inland waters, wild animals, termites, land geological
sources, oceanic geological and biogenic sources, and terrestrial
permafrost). Anthropogenic emissions contain “agriculture and waste
emissions”, “fossil fuel emissions”, and “biomass and biofuel burning
emissions”, assuming that all types of fires cause anthropogenic sources,
although they are partly of natural origin (Fig. 6; see also Tables 3 and 6).
Our definition of natural and anthropogenic sources does 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 reported as anthropogenic, which is not the
case here. For instance, we consider all wetlands to be natural emissions,
despite some wetlands being managed and their emissions being partly
reported in UNFCCC national communications. The human-induced perturbation
of climate, atmospheric
CO
, and nitrogen and sulfur deposition may cause
changes in the sources we classified as natural. Following our definition,
emissions from wetlands, inland water, or thawing permafrost will be
accountable in natural emissions, even though we acknowledge that
climate change – a human perturbation – may cause increasing emissions
from these sources. Methane emissions from reservoirs are considered
natural even though reservoirs are human-made, and since the 2019 refinement
to the IPCC guidelines (IPCC, 2006, 2019)
emissions from reservoirs and other flooded lands are considered
anthropogenic by the UNFCCC.
Following Saunois et al. (2016), we report anthropogenic and natural methane
emissions for five main source categories for both bottom-up and top-down
approaches.
Bottom-up estimates of methane 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 subcategories inside each main GCP category (see budget in Table 3).
However, the total methane emission derived from the sum of independent
bottom-up estimates remains unconstrained.
For atmospheric inversions (top-down approach) the situation is different.
Atmospheric observations provide a constraint on the global total source and
a reasonable constraint on the global sink derived from methyl chloroform
(Montzka
et al., 2011; Rigby et al., 2017). The inversions reported in this work
solve either for a total methane flux (e.g. Pison et
al., 2013) or for a limited number of source categories
(e.g.
Bergamaschi et al., 2013). In most
of the inverse systems the atmospheric oxidant concentrations are prescribed
with pre-optimized or scaled OH fields, and thus the atmospheric sink is not
solved. The assimilation of
CH
observations alone, as reported in this
synthesis, can help to separate sources with different locations or temporal
variations but cannot fully separate individual sources as they often
overlap in space and time in some regions. Top-down global and regional
methane emissions per source category were obtained directly from gridded
optimized fluxes, wherever an inversion had solved for the separate five
main GCP categories. Alternatively, if an inversion only solved for total
emissions (or for categories other than the main five described above), then
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)
optimized 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 while updating total emissions with atmospheric
observations. The soil uptake was provided separately in order to report
total gross surface emissions instead of net fluxes (sources minus soil
uptake).
In summary, bottom-up models and inventories are presented for all source
processes and for the five main categories defined above globally. Top-down
inversions are reported globally and only for the five main emission
categories.
Table 1
Bottom-up models and inventories for anthropogenic and biomass burning
estimates used in this study.
Due to its limited sectorial breakdown this
dataset was used in Table 3 for the main categories only, replacing CEDS country-based estimates.
Extended to 2017 for this
study as described in Sect. 3.1.1.
Download Print Version
Download XLSX
2.5
Processing of emission maps and box-plot 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
grid or re-gridded to
, then converted into units
of teragrams of methane per grid cell. Inversions with a resolution coarser than
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
maps.
Budgets are presented as box plots 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
inter-quartile range, or values above the third quartile plus 3 times
the inter-quartile range. Mean values reported in the tables are represented
as “
” symbols in the corresponding figures.
Methane sources and sinks: bottom-up estimates
For each source category, a short description of the relevant processes,
original datasets (measurements, models), and related methodology is given.
More detailed information can be found in original publication references
and in the Supplement of this study.
3.1
Anthropogenic sources
3.1.1
Global inventories gathered
The main bottom-up global inventory datasets covering anthropogenic
emissions from all sectors (Table 1) are from the United States
Environmental Protection Agency (USEPA, 2012), the Greenhouse gas
and Air pollutant Interactions and Synergies (GAINS) model developed by the
International Institute for Applied Systems Analysis (IIASA)
(Gomez Sanabria et al., 2018;
Höglund-Isaksson, 2012, 2017), and the Emissions Database for Global
Atmospheric Research (EDGARv3.2.2;
Janssens-Maenhout et al., 2019) compiled by the European Commission Joint
Research Centre (EC-JRC) and Netherland's 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
Organization (FAO) dataset emission database (Tubiello, 2019),
which only covers emissions from agriculture and land use (including
peatland and biomass fires).
These inventory datasets report emissions from fossil fuel production,
transmission, and distribution; livestock enteric fermentation; manure
management and application; rice cultivation; 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 and 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
yr
−1
) in recent
decades to prevent any inadvertent overlap with separate estimates of
biomass burning emissions (e.g. GFEDv4.1s). 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 an emission factor for this
activity. An abatement coefficient is used additionally, to account for any
regulations implemented to control emissions (see
e.g. Höglund-Isaksson et al., 2015). These datasets differ in their
assumptions and data used for the calculation; however, they are not
completely independent because they follow the same IPCC guidelines
(IPCC, 2006), and, at least for agriculture, use
the same FAOSTAT activity data. 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. These other inventories compile country-specific activity
data and emission factor information or, if not available, adopt IPCC
default factors
(Höglund-Isaksson, 2012;
Janssens-Maenhout et al., 2019; Tubiello, 2019). The CEDS takes a different
approach starting from pre-existing default emission estimates; for methane,
a combination of EDGAR and FAO estimates is used, 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 dataset
(hereafter FAO-
CH
) was used to provide estimates of methane emissions
at the country level but is limited to agriculture (enteric fermentation, manure
management, rice cultivation, energy usage, burning of crop residues, and
prescribed burning of savannahs) and land use (biomass burning).
FAO-
CH
uses activity data mainly from the FAOSTAT crop and livestock
production database, as reported by countries to the 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 necessary
country-scale temperature was obtained from the FAO global agroecological
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 databases (see Table 1):
EDGARv4.3.2, which provides yearly gridded emissions by sectors from 1970 to
2012 (Janssens-Maenhout et al., 2019);
GAINS model scenario ECLIPSE v6 (Gomez Sanabria
et al., 2018; Höglund-Isaksson, 2012, 2017), which provides both annual
sectoral totals by country from 1990 to 2015 and a projection for 2020 (that
assumes current emission legislation for the future) and an annual sectorial
gridded product from 1990 to 2015;
USEPA (USEPA, 2012), which provides 5-year sectorial totals by
country from 1990 to 2020 (estimates from 2005 onward are a projection),
with no gridded distribution available;
CEDS version 2017-05-18, which provides both gridded monthly and annual
country-based emissions by sectors from 1970 to 2014
(Hoesly et al., 2018);
FAO-
CH
(database accessed in February 2019, FAO, 2019)
containing annual country-level data for the period 1961–2016, for rice,
manure, and enteric fermentation and 1990–2016 for burning savannah, crop
residue, and non-agricultural biomass burning.
In order to report emissions for the period 2000–2017, we extended and
interpolated some of the datasets as explained in Sect. 2.2. The USEPA
dataset was linearly interpolated to provide yearly values. The FAO-
CH
dataset, ending in 2016, was extrapolated to 2017 using a linear fit based
on 2014–2016 data. EDGARv4.3.2 was extrapolated to 2017 using the
extended FAO-
CH
emissions for enteric fermentation, manure management,
and rice cultivation and using the BP statistical review of fossil fuel
production and consumption (BP Statistical Review of World Energy, 2019) for
emissions from the coal, oil, and gas sectors. In this extrapolated inventory,
called EDGARv4.3.2
EXT
, methane emissions for year
are set equal to the
2012 (last year) EDGAR emissions (E
EDGARv4.3.2
) times the ratio
between FAO-
CH
emissions (or BP statistics) of year
(E
FAO-
CH
) and FAO-
CH
emissions (or BP statistics) of 2012
(E
FAO-
CH
(2012)). For each emission sector, region-specific emissions of
EDGARv4.3.2
EXT
in year
are estimated following Eq. (1):
(1)
EDGARv4.3.2ext
EDGARv4.3.2
2012
FAO-
CH
FAO-
CH
2012
Transport, industrial, waste, and biofuel sources were linearly extrapolated
in EDGARv4.3.2
EXT
based on the last 3 years of data while other
sources were kept constant at the 2012 level. To allow comparisons through
2017, the CEDS dataset has also been extrapolated in an identical method
creating CEDS
EXT
. However, in contrast to the EDGARv4.3.2 dataset, the CEDS
dataset provides only a combined oil and gas sector; hence, we extended this
sector using the sum of BP oil and gas emissions. The by-country GAINS
dataset was linearly projected by sector for each country using the trend
between the historical 2015 and projected 2020 values. These by-country
projections were aggregated to the 19 global regions (Sect. 2.3 and Fig. S1) and used to extrapolate the GAINS gridded dataset in a similar manner to
that described in Eq. (1). Although we only use the extended inventories,
in the following the “EXT” suffix will be dropped for clarity.
3.1.2
Total anthropogenic emissions
In order to avoid double-counting and ensure consistency with each
inventory, the range (min–max) and mean values of the total anthropogenic
emissions were not calculated as the sum of the mean and range of the three
anthropogenic categories (“agriculture and waste”, “fossil fuels”, and
“biomass burning & biofuels”). Instead, we calculated separately the
total anthropogenic emissions for each inventory by adding its values for
agriculture and waste, fossil fuels, and biofuels with the range
of available large-scale biomass burning emissions. This approach was used
for the EGDARv4.3.2, CEDS, and GAINS inventories, but we kept the USEPA
inventory as originally reported because it includes its own estimates of
biomass burning emissions. FAO-
CH
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 & 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 sub-sector categorizations between inventories.
Based on the ensemble of databases detailed above, total anthropogenic
emissions were 366 [349–393] Tg
CH
yr
−1
for the decade 2008–2017
(Table 3, including biomass and biofuel burning) and 334 [321–358] Tg
CH
yr
−1
for the decade 2000–2009. Our estimate for the preceding
decade is statistically consistent with Saunois et al. (2016)
(338 Tg
CH
yr
−1
[329–342]) and Kirschke et al. (2013)
(331 Tg
CH
yr
−1
[304–368]) for the same period. The slightly
larger range reported herein with respect to previous estimates is mainly
due to a larger range in the biomass burning estimates, as more biomass
burning products are included in this update. The range associated with our
estimates (
10 %–12 %) is smaller than the range reported in
Höglund-Isaksson et al. (2015) (
20 %),
perhaps because they analysed data from a wider range of inventories and
projections, plus this study was referenced to one year only (2005) rather
than averaged over a decade, as done here.
Figure 2
(a, b)
Global anthropogenic methane emissions (including biomass
burning) from historical inventories and future projections (Tg
CH
yr
−1
).
(a)
Inventories and the unharmonized Shared
Socioeconomic Pathways (Riahi et
al., 2017), with highlighted scenarios representing scenarios assessed in
CMIP6 (O'Neill, et al., 2016).
(b)
The selected scenarios
harmonized with historical emissions (CEDS) for CMIP6 activities
(Gidden et al., 2019). USEPA and GAINS estimates
have been linearly interpolated from the 5-year original products to yearly
values. After 2005, USEPA original estimates are projections.
(c)
Global 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.,
2011).
Figure 2a summarizes global methane emissions of anthropogenic
sources (including biomass and biofuel burning) by different datasets
between 2000 and 2050. The datasets consistently estimate total
anthropogenic emissions of
300 Tg
CH
yr
−1
in
2000. The main discrepancy between the inventories is their trend after
2005, with the lowest emissions projected by GAINS and the largest by CEDS. With the U.S. EPA being a projection from 2005 onward, its values and trends deviate from others.
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 Phase 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 by the number in the SSP names). The trends in
methane emissions from 2010 estimated by current inventories track the
pathways with the highest radiative forcing in 2100 (based on the
unharmonized scenarios developed by integrated assessment models, Fig. 2a). 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 CEDS anthropogenic emissions estimates, based on EDGARv4.2, are
10–20 Tg higher than the more recent EDGARv4.3.2
(van Marle et al., 2017).
Harmonized scenarios used for CMIP6 activities start in 2015 at 388 Tg
CH
yr
−1
. Since methane emissions continue to track scenarios that
assume no or minimal climate policies, 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
over the next decade (Fig. 2b). This illustrates the challenge of methane mitigation
that lies ahead to help reach the goals of the Paris Agreement. In addition,
estimates of methane atmospheric concentrations from the unharmonized
scenarios (Riahi et al., 2017)
indicate that observations of global methane concentrations fall well within
the range of scenarios (Fig. 2c). The methane 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. In the future, it will be
important to
monitor the trends from the year 2015 (the Paris Agreement)
estimated in inventories and from atmospheric observations and compare them
to various scenarios.
3.1.3
Fossil fuel production and use
Most anthropogenic methane 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 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). Methane emissions from the oil
industry (e.g. refining) and production of charcoal are estimated to be a
few teragrams of methane per year only and are included in the transformation
industry sector in the inventory. Fossil fuel fires are included in the
subcategory “oil & gas”. Emissions from industries and road and
non-road transport are reported apart from the two main subcategories oil
& gas and “coal mining”, contrary to Saunois et al. (2016); each of these
amounts to about 5 Tg
CH
yr
−1
(Table 3). The large range (0–12 Tg
CH
yr
−1
) is attributable to difficulties in allocating some
sectors to these sub-sectors consistently among the different inventories
(see Table S2). The spatial distribution of methane emissions from fossil
fuels is presented in Fig. 3 based on the mean gridded maps provided by
CEDS, EDGARv4.3.2, and GAINS for the 2008–2017 decade; the USEPA lacks a gridded
product.
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 2008–2017 decade (mg
CH
−2
−1
). The wetland emission map represents the mean
daily emission average over the 13 biogeochemical models listed in Table 2
and over the 2008–2017 decade. Fossil fuel and agriculture and waste
emission maps are derived from the mean estimates of gridded CEDS,
EGDARv4.3.2, 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, EDGARv4.3.2, and GAINS models.
Global mean emissions from fossil-fuel-related activities, other industries,
and transport are estimated from the four global inventories (Table 1) to be
of 128 [113–154] Tg
CH
yr
−1
for the 2008–2017 decade (Table 3),
but with large differences in the rate of change during this period across
inventories. The sector accounts on average for 35 % (range 30 %–42 %) of
total global anthropogenic emissions.
Coal mining
During mining, methane is emitted primarily from ventilation shafts, where
large volumes of air are pumped into the mine to keep the
CH
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), methane released from ventilation shafts is in principle
used as fuel, but in many countries, it is still emitted into the atmosphere
or flared, despite the efforts for coal mine recovery under the UNFCCC Clean
Development Mechanisms (
, last access: 29 June 2020). Methane also leaks occur
during post-mining handling, processing, and transportation. Some
CH
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 to be 22 billion m
(compared with 103 billion m
from functioning coal mines) in 2010 with emissions
projected to increase into the future (Kholod et al.,
2020).
In 2017, almost 40 % (IEA, 2019b) of the world's electricity
was still produced from coal. This contribution grew in the 2000s at the rate
of several per cent per year, driven by Asian economic growth where large
reserves exist, but global coal consumption has declined since 2014. In 2018,
the top 10 largest coal producing nations accounted for
90 % of total world methane emissions for coal mining; among them, the top
three producers (China, United States, and India) produced almost two-thirds (64 %)
of the world's coal (IEA, 2019a).
Global estimates of
CH
emissions from coal mining show a large range
of 29–61 Tg
CH
yr
−1
for 2008–2017, in part due to the lack of
comprehensive data from all major producing countries. The highest value of
the range comes from the CEDS inventory while the lowest comes from the USEPA.
CEDS seems to have overestimated coal mining emissions from China by almost
a factor of 2, most likely due to its dependence on the EDGARv4.2 emission
inventory. As highlighted by Saunois et al. (2016),
a county-based inventory of Chinese methane emissions also confirms the
overestimate of about
38 % with total anthropogenic emissions estimated
at
43±6
Tg
CH
yr
−1
(Peng et al.,
2016). The EDGARv4.2 inventory follows the IPCC guidelines and uses a European
averaged emission factor for
CH
from coal production to substitute
missing data for China, which appear to be overestimated by a factor of
approximately 2. These differences highlight significant errors resulting
from the use of emission factors, and applying Tier 1 approaches
for coal mine emissions is not sufficiently accurate as stated by the IPCC
guidelines. The newly released version of EDGARv4.3.2 used here has revised
China coal methane emission factors downwards and distributed them to more
than 80 times more coal mining locations in China. Coal mining emission
factors depend strongly on the type of coal extraction (underground mining
emits up to 10 times more than surface mining), geological underground
structure (region-specific), history (basin uplift), and quality of the
coal (brown coal emits more than hard coal). Finally, coal mining is the
main source explaining the differences between inventories globally (Fig. 2).
For the 2008–2017 decade, methane emissions from coal mining represent
33 % of total fossil-fuel-related emissions of methane (42 Tg
CH
yr
−1
, range of 29–61). An additional very small source corresponds to
fossil fuel fires (mostly underground coal fires,
0.15 Tg yr
−1
in 2012, EDGARv4.3.2).
Oil and natural gas systems
This subcategory includes emissions from both conventional and shale oil
and gas exploitation. Natural gas is comprised primarily of methane, so both
fugitive and planned emissions during the drilling of wells in gas fields,
extraction, transportation, storage, gas distribution, end use, and
incomplete
combustion of gas flares emit methane
(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). 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),
CH
fugitive emissions from gas pipelines should be relatively low; however
distribution networks in older cities may have higher rates, especially
those with cast-iron and unprotected steel pipelines
(Phillips et al., 2013).
Measurement campaigns in cities within the United States and Europe revealed that
significant emissions occur in specific locations (e.g. storage facilities,
city gates, well and pipeline pressurization–depressurization points) along
the distribution networks
(e.g.
Jackson et al., 2014a; McKain et al., 2015; Wunch et al., 2016). However,
methane 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. In many facilities, such as
gas and oil fields, refineries, and offshore platforms, venting of natural
gas is now replaced by flaring with almost complete conversion to
CO
these two processes are usually considered together in inventories of oil
and gas industries. Also, single-point failure of natural gas infrastructure
can leak methane at a high rate for months, such as at the Aliso Canyon
blowout in the Los Angeles, CA, basin
(Conley et al., 2016) or the recent
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 United States on an experimental or small-scale basis,
and then, from the early 2000s, exploitation started at large commercial scale.
The shale gas contribution to total dry natural gas production in the United
States reached 62 % in 2017, growing rapidly from 40 % in 2012, with
only small volumes produced before 2005 (EIA, 2019). The
possibly larger emission factors from the shale gas compared to the
conventional ones have been widely debated
(e.g.
Cathles et al., 2012; Howarth, 2019; Lewan, 2020). However, the latest
studies tend to infer similar emission factors in a narrow range of 1 %–3 %
(Alvarez
et al., 2018; Peischl et al., 2015; Zavala-Araiza et al., 2015), different
from the widely spread rates of 3 %–17 % from previous studies
(e.g. Caulton et al.,
2014; Schneising et al., 2014).
Methane emissions from oil and natural gas systems vary greatly in different
global inventories (72 to 97 Tg yr
−1
in 2017, 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) and remain largely
unknown
for most major oil- and gas-producing countries. Depending on the
country, the reported emission factors may vary by 2 orders of magnitude
for oil production and by 1 order of magnitude for gas production (Table S5.1 of Höglund-Isaksson, 2017). The GAINS estimate of
methane emissions from oil production, for instance, is twice as high as
EDGARv4.3.2. For natural gas, the uncertainty is of a similar order of
magnitude. During oil extraction, natural gas generated can be either
recovered (re-injected or utilized as an energy source) or not recovered
(flared or vented to the atmosphere). The recovery rates vary from one
country to another (being much higher in the United States, Europe, and Canada than
elsewhere) and from one type of oil to another: flaring is less common for
heavy oil wells than for conventional ones
(Höglund-Isaksson et al., 2015). Considering recovery
rates could lead to 2-times-higher methane emissions accounting for
country-specific rates of generation and recovery of associated gas than
when using default values (Höglund-Isaksson, 2012). This
difference in methodology explains, in part, why GAINS estimates are higher
than those of EDGARv4.3.2.
Most studies (Alvarez
et al., 2018; Brandt et al., 2014; Jackson et al., 2014b; Karion et al.,
2013; Moore et al., 2014; Olivier and Janssens-Maenhout, 2014; Pétron et
al., 2014; Zavala-Araiza et al., 2015), albeit not all
(Allen
et al., 2013; Cathles et al., 2012; Peischl et al., 2015), suggest that
methane emissions from oil and gas industry are underestimated by
inventories and agencies, including the USEPA. Zavala-Araiza et al. (2015) showed that a few
high-emitting facilities, i.e., super-emitters, neglected in the
inventories, dominated US emissions. These high-emitting points, located on
the conventional part of the facility, could be avoided through better
operating conditions and repair of malfunctions. As US production
increases, absolute methane emissions almost certainly increase. US
crude oil production also doubled over the last decade and natural gas production
rose more than 50 % (EIA, 2019). However, global implications
of the rapidly growing shale gas activity in the United States remain to be determined
precisely.
For the 2008–2017 decade, methane emissions from upstream and downstream oil
and natural gas sectors are estimated to represent about 63 % of total
fossil
CH
emissions (80 Tg
CH
yr
−1
, range of 68–92 Tg
CH
yr
−1
, Table 3), with a lower uncertainty range than for coal
emissions for most countries.
3.1.4
Agriculture and waste sectors
This main category includes methane emissions related to livestock
production (i.e., enteric fermentation in ruminant animals and manure
management), rice cultivation, landfills, and wastewater handling. Of these,
globally and in most countries, livestock is by far the largest source of
CH
, followed by waste handling and rice cultivation. Conversely, field
burning of agricultural residues is a minor source of
CH
reported in
emission inventories. The spatial distribution of methane emissions from
agriculture and waste handling is presented in Fig. 3 based on the mean
gridded maps provided by CEDS, EDGARv4.3.2, and GAINS over the 2008–2017
decade.
Global emissions from agriculture and waste for the period 2008–2017 are
estimated to be 206 Tg
CH
yr
−1
(range 191–223, Table 3),
representing 56 % of total anthropogenic emissions.
Livestock: enteric fermentation and manure management
Domestic ruminants
such as cattle, buffalo, sheep, goats, and camels emit methane 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) values 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 methane. Methane is released from the rumen mainly through the mouth
of multi-stomached ruminants (eructation,
87 % of
emissions) or absorbed in the blood system. The methane produced in the
intestines and partially transmitted through the rectum is only
13 %.
The total number of livestock continues to grow steadily. There are
currently (2017) about 1.5 billion cattle globally, 1 billion sheep, and
nearly as many goats (
, last access: 29 June 2020). Livestock
numbers are linearly related to
CH
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 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
CH
emissions from livestock worldwide (Tubiello, 2019),
particularly in intensive agricultural systems in wealthier and emerging
economies, including the United States (USEPA, 2016). Methane
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 characterize 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
CH
. 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
CH
. However aerobic
decomposition of manure tends to produce nitrous oxide (
), which has
a larger warming impact than
CH
. Ambient temperature, moisture, energy
contents of the feed, manure composition, and manure storage or residency
time affect the amount of
CH
produced. Despite these complexities,
most global datasets 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 methane emissions from enteric fermentation and manure management are
estimated in the range of 99–115 Tg
CH
yr
−1
, for the year 2010,
in the GAINS model and CEDS, USEPA, FAO-
CH
, and EDGARv4.3.2
inventories. These values are slightly higher than the IPCC Tier 2 estimate
of Dangal et al. (2017)
(95.7 Tg
CH
yr
−1
for 2010) and the IPCC Tier 3 estimates of Herrero et
al. (2013) (83.2 Tg
CH
yr
−1
for 2000),
but in agreement with the recent IPCC Tier 2 estimate of Chang et al. (2019) (
99±12
Tg
CH
yr
−1
for 2012).
For the period 2008–2017, we estimated total emissions of 111 [106–116] Tg
CH
yr
−1
for enteric fermentation and manure management, about one-third of total global anthropogenic emissions.
Rice cultivation
Most of the world's rice is grown in flooded paddy fields
(Baicich, 2013). The water management systems, particularly
flooding, used to cultivate rice are one of the most important factors
influencing
CH
emissions and one of the most promising approaches for
CH
emission mitigation: periodic drainage and aeration not only cause
existing soil
CH
to oxidize, but also inhibit further
CH
production in soils
(Simpson et al.,
1995; USEPA, 2016; Zhang, 2016). Upland rice fields are not typically
flooded and therefore are not a significant source of
CH
. Other
factors that influence
CH
emissions from flooded rice fields include
fertilization practices (i.e. the use of urea and organic fertilizers), soil
temperature, soil type (texture and aggregated size), rice variety, and
cultivation practices (e.g., tillage, seeding, and weeding practices)
(Conrad et al.,
2000; Kai et al., 2011; USEPA, 2011; Yan et al., 2009). For instance,
methane emissions from rice paddies increase with organic amendments
(Cai et al., 1997) but can be mitigated by applying other
types of fertilizers (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 or
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, 2016). The emissions show a seasonal cycle, peaking
in the summer months in the extra-tropics associated with monsoons and land
management. Similar to emissions from livestock, emissions from rice paddies
are influenced not only by extent of rice field area (analogous to livestock
numbers), but also by changes in the productivity of plants
(Jiang et al., 2017) as these alter the
CH
emission factor used in inventories. Nonetheless, the inventories
considered herein are largely based on IPCC Tier 1 methods, which largely
scale with cultivated areas but include region-specific emission factors.
The largest emissions from rice cultivation are found in Asia, accounting for
30 % to 50 % of global emissions (Fig. 3). The decrease in
CH
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 methane
emissions from rice paddies are estimated to be 30 [25–38] Tg
CH
yr
−1
for the 2008–2017 decade (Table 3), or about 8 % of total global
anthropogenic emissions of methane. These estimates are consistent with the
29 Tg
CH
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. Methane production from
waste depends on the pH, moisture, and temperature of the material. The
optimum pH for methane 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, 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
anthropogenic methane emissions in 2000
(Kirschke
et al., 2013). A recent assessment of methane emissions in the United States found
landfills to account for almost 26 % of total US anthropogenic methane
emissions in 2014, the largest contribution of any single
CH
source in
the United States (USEPA, 2016). In Europe, gas control has
been mandatory on all landfills since 2009, following the ambitious
objective raised in the EU Landfill Directive (1999) to reduce
landfilling of biodegradable waste to 65 % below the 1990 level by 2016.
This mitigation is attempted through source separation and treatment of
separated biodegradable waste in composts, bio-digesters, and paper
recycling.
Wastewater from domestic and industrial sources is treated in municipal
sewage treatment facilities and private effluent treatment plants. The
principal factor in determining the
CH
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 29–41 Tg
CH
yr
−1
for
the year 2005 and more uncertain wastewater emissions in the range 14–33 Tg
CH
yr
−1
1.
In our study, the global emission of methane from waste management is
estimated in the range of 60–69 Tg
CH
yr
−1
for the 2008–2017
period with a mean value of 65 Tg
CH
yr
−1
, about 12 % of total
global anthropogenic emissions.
3.1.5
Biomass and biofuel burning
This category includes methane emissions from biomass burning in forests,
savannahs, grasslands, peats, agricultural residues, and the
burning of biofuels in the residential sector (stoves, boilers, fireplaces).
Biomass and biofuel burning emits methane 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 methane emitted during the burning of biomass depends
primarily on the amount of biomass, burning conditions, 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 (EDGARv4.3.2, CEDS, GAINS, and USEPA). The spatial distribution
of emissions from the burning of biomass and biofuel over the 2008–2017
decade is presented in Fig. 3 based on data listed in Table 1.
At the global scale, during the period of 2008–2017, biomass and biofuel
burning generated methane emissions of 30 [26–40] Tg
CH
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).
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 well detected by remote sensing
methods and are instead estimated based on cultivated area.
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 (smoldering 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 a 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 dataset and provides estimates from 1997. Here, we
use GFEDv4.1s (van
der Werf et al., 2017), based on the Carnegie–Ames–Stanford approach (CASA)
biogeochemical model and satellite-derived estimates of burned area (from
the MODerate resolution Imaging Sensor, MODIS), fire activity, and plant
productivity. GFEDv4.1s (with small fires) is available at a 0.25
resolution and on a daily basis from 1997 to 2017. 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 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 methane 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
(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 2017.
The Fire Inventory from NCAR
(FINN; Wiedinmyer et al., 2011)
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 2002–2018. FINNv1.5 uses
MODIS 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 2000–2016.
The FAO-
CH
yearly biomass burning emissions are based on the most
recent MODIS 6 burned-area products, coupled with a pixel-level (500 m)
implementation of the IPCC Tier 1 approach, and are available from 1990 to
2016 (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
CH
kg
−1
dry matter burned for savannah and grassland fires up to 21 g
CH
kg
−1
dry matter burned for peat fires
(van
der Werf et al., 2010).
In this study, based on the five aforementioned products, biomass burning
emissions are estimated at 17 Tg
CH
yr
−1
[14–26] for 2008–2017,
representing about 5 % of total global anthropogenic methane emissions.
Biofuel burning
Biomass that is used to produce energy for domestic,
industrial, commercial, or transportation purposes is hereafter called
biofuel burning. A largely dominant fraction of methane emissions from
biofuels 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), and
yet methane 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) (see Table S2). This is
equivalent to the sum of the IPCC 1A4a_Commercial-institutional, 1A4b_Residential,
1A4c_Agriculture-forestry-fishing, and 1A5_Other-unspecified 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 %.
In our study, biofuel burning is estimated to contribute 12 Tg
CH
yr
−1
[10–14] to the global methane budget, about 3 % of total global
anthropogenic methane emissions for 2008–2017.
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 global palm
oil production have led to the clearing of natural peat forests, reducing
natural peatland area and associated natural
CH
emissions. While
studies have long suggested that
CH
emissions from peatland drainage
ditches are likely to be significant (e.g. Minkkinen and Laine,
2006),
CH
emissions related to palm oil plantations have yet to be
properly quantified. Taylor et al. (2014) have
quantified global palm oil wastewater treatment fluxes to be
4±32
Tg
CH
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 sources
Natural methane sources include vegetated wetland emissions and inland water
systems (lakes, small ponds, rivers), land geological sources (gas–oil
seeps, mud volcanoes, microseepage, geothermal manifestations, and
volcanoes), wild animals, termites, thawing terrestrial and
marine permafrost, and oceanic sources (biogenic, geological, and hydrate). In
water-saturated or flooded ecosystems, the decomposition of organic matter
gradually depletes most of the oxygen in the soil, resulting in anaerobic
conditions and methane production. Once produced, methane can reach the
atmosphere through a combination of three processes: (1) diffusive loss of
dissolved
CH
across the air–water boundary; (2) ebullition flux from
sediments, and (3) flux mediated by emergent aquatic macrophytes and
terrestrial plants (plant transport). On its way to the atmosphere, in the
soil or water columns, methane can be partly or completely oxidized by a
group of bacteria called methanotrophs, which use methane as their only
source of energy and carbon (USEPA, 2010b).
Concurrently, methane from the atmosphere can diffuse into the soil column
and be oxidized (see Sect. 3.3.4 on soil uptake).
3.2.1
Wetlands
Wetlands are generally defined as ecosystems in which soils or peats are
water saturated or where surface inundation (permanent or not) dominates the
soil biogeochemistry and determines the ecosystem species composition
(USEPA, 2010b). In order to refine such overly broad
definition for methane emissions, we define wetlands as ecosystems with
inundated or saturated soils or peats where anaerobic conditions lead to
methane production (Matthews and
Fung, 1987; USEPA, 2010b). Brackish water emissions are discussed separately
in Sect. 3.2.6. Our definition of wetlands includes peatlands (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 psu (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 impoundments, 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 methane 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).
Land surface models estimate
CH
emissions through a series of
processes, including
CH
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). Methane emissions from wetlands are computed as the
product of an emission flux density and a methane-producing area or surface
extent
(see Supplement; Bohn et al., 2015; Melton et al., 2013). Wetland
extent appears to be a primary contributor to uncertainties in the absolute
flux of methane emissions from wetlands, with meteorological response the
main source of uncertainty for seasonal and inter-annual variability
(Bohn
et al., 2015; Desai et al., 2015; Poulter et al., 2017).
Table 2
Biogeochemical models that computed wetland emissions used in this
study. Runs were performed for the whole period 2000–2017. Models run with
prognostic (using their own calculation of wetland areas) and/or diagnostic
(using WAD2M) wetland surface areas (see Sect. 3.2.1).
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In this work, 13 land surface models computing net
CH
emissions
(Table 2) were run under a common protocol with a 30-year spin-up
(1901–1930) followed by a simulation through the end of 2017 forced by
CRU-JRA reconstructed climate fields (Harris, 2019). Of the 13 models, 10
previously contributed to Saunois et al. (2016), three models were new to
this release (JSBACH, LPJ-GUESS, and TEM-MDM) (Table S3). Atmospheric
CO
influencing wetland net primary production (NPP) was also
prescribed in the models. In all models, the same remote-sensing-based
wetland area and dynamics dataset called WAD2M (Wetland Area Dynamics for
Methane Modeling) was prescribed. WAD2M provides year-to-year varying
monthly global wetland areas over 2000–2017, partly addressing known issues,
such as separation between wetlands and other inland waters
(Poulter
et al., 2017). WAD2M combines microwave remote sensing data from Schroeder
et al. (2015) with various regional inventory datasets to
develop a monthly global wetland area dataset, which will be further
presented in the near future by Zhang et al. (2020). Non-vegetated
wetland inland waters (i.e., lakes, rivers, and ponds) were subtracted using
the Global Surface Water dataset of Pekel et al. (2016),
assuming that permanent waters were those that were present
50 % of the time within a 32-year observing period. Then, wetland
inventories for the tropics (Gumbricht et al., 2017),
high latitudes (Hugelius et al.,
2014; Widhalm et al., 2015), and temperate regions (Lehner and
Döll, 2004) were used to set the long-term annual mean wetland area, to
which a seasonal cycle of fractional surface water was added using data from
the Surface Water Microwave Product Series version 3.2 (SWAMPS)
(Jensen and Mcdonald, 2019;
Schroeder et al., 2015). Rice agriculture was removed using the MIRCA2000
dataset from circa 2000, as a fixed distribution. The combined
remote sensing and inventory WAD2M product leads to a maximum wetland area
of 14.9 M km
during the peak season (8.4 M km
on annual average,
with a range of 8.0 to 8.9 M km
from 2000 to 2017, about 5.5 % of the
global land surface). The largest wetland areas in WAD2M are in Amazonia,
the Congo Basin, and the Western Siberian Lowlands, which in previous
studies were underestimated by inventories
(Bohn et al., 2015).
The average emission map from wetlands for 2008–2017 built from the 13
models is plotted in Fig. 3. The zones with the largest emissions are the
Amazon basin, equatorial Africa and Asia, Canada, western Siberia, eastern
India, and Bangladesh. Regions where methane emissions are robustly inferred
(defined as regions where mean flux is larger than the standard deviation of
the models) represent 61 % of the total methane flux due to natural
wetlands. This contribution is 80 % lower than found in Saunois et al. (2016) probably due to the different ensemble of models gathered here and
the more stringent exclusion of inland waters. The main primary emission
zones are consistent between models, which is clearly favoured by the
prescribed common wetland extent. However, the different sensitivities of
the models to temperature, vapour pressure, precipitation, and radiation can
generate substantially different patterns, such as in India. Some secondary
(in magnitude) emission zones are also consistently inferred between models:
Scandinavia, continental Europe, eastern Siberia, central United States, and tropical
Africa.
The resulting global flux range for natural wetland emissions is 101–179 Tg
CH
yr
−1
for the 2000–2017 period, with an average of 148 Tg
CH
yr
−1
and a one-sigma standard deviation of 25 Tg
CH
yr
−1
. For the last decade, 2008–2017, the average ensemble emissions
were 149 Tg
CH
yr
−1
with a range of 102–182 (Table 3). Using a
prognostic set of simulations, where models used their own internal approach
to estimate wetland area and dynamics, the average ensemble emissions were
161 Tg
CH
yr
−1
with a range of 125–218 for the 2008–2017
period. The greater range of uncertainty from prognostic area models is due
to unconstrained wetland area, but generally the magnitude and inter-annual
variability agree between diagnostic and prognostic area approaches. Wetland
emissions represent about 20 % of the total (natural plus anthropogenic)
methane sources estimated by bottom-up approaches. The large range in the
estimates of wetland
CH
emissions results from difficulties in
defining wetland
CH
-producing areas as well as in parameterizing
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 diagnostic wetland extent in the models is lower by
35 Tg
CH
yr
−1
than the one previously reported
(see Table 3, for 2000–2009 with comparison to Saunois et al., 2016). This
difference results from a reduction in double-counting due to (i) decreased
wetland area in WAD2M, especially for high-latitude regions where inland
waters, i.e., lakes, small ponds, and lakes, were removed, and (ii) to some
extent, an improved removal of rice agriculture area using the MIRCA2000
database.
For the last decade, 2008–2017, the average ensemble emissions were 149 Tg
CH
yr
−1
with a range of 102–182.
Table 3
Global methane emissions by source type (Tg
CH
yr
−1
from Saunois et al. (2016) (left column pair) and for 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 the [min–max] range of
reported studies. Differences of 1 Tg
CH
yr
−1
in the totals can
occur due to rounding errors.
Freshwater includes lakes, ponds, reservoirs, streams, and rivers.
Includes flux from hydrates considered at 0 for this study, includes
estuaries.
For IIASA inventory the breakdown of agriculture and waste (rice, enteric
fermentation & manure, landfills & waste) and fossil fuel (coal, oil,
gas & industry) sources used the same ratios as the mean of the EDGAR and
USEPA inventories in Saunois et al. (2016).
Total sink was deduced from global mass balance and not directly computed
in Saunois et al. (2016).
Computed as the difference of global sink and soil uptake in Saunois et
al. (2016).
Industry and transport emissions were included in the oil & gas
category in Saunois et al. (2016).
Total anthropogenic emissions are based on estimates of a full anthropogenic
inventory and not on the sum of the “agriculture and waste”, “fossil fuels”,
and “biofuel and biomass burning” categories (see Sect. 3.1.2).
Some inversions did not provide the chemical sink. These values are
derived from a subset of the inversion ensemble.
Atmospheric growth is given in the same unit (Tg
CH
yr
−1
),
based on the conversion factor of 2.75 Tg
CH
ppb
−1
given by
Prather et al. (2012) and the atmospheric growth rates provided in the text
in parts per billion per year.
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).
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3.2.2
Other inland water systems (lakes, ponds, reservoirs, streams, rivers)
This category includes methane emissions from freshwater systems (lakes,
ponds, reservoirs, streams, and rivers). To date, very few process-based
models exist for these fluxes, relying on data-driven approaches and
extrapolations. Meta-data analyses are hampered for methane due to a mix of
methodological approaches, which capture different components of emissions,
and different scales in space and time, depending on method and time of
deployment and data processing (Stanley et al., 2016).
Altogether, this inconsistency in the data collection makes detailed
modelling of fluxes highly uncertain. For many lakes, particularly smaller
shallower lakes and ponds, it is established that ebullition and plant
fluxes (in lakes with substantial emergent macrophyte communities) can make
up a substantial contribution to fluxes, potentially accounting for 50 %
to more than 90 % of the flux from these water bodies. While contributions
from ebullition appear lower from rivers, there are currently insufficient
measurements from these systems to determine its role
(Crawford et al., 2014; Stanley et al.,
2016). Ebullition fluxes are very challenging to measure, due to the high
degree of spatio-temporal variability with very high fluxes occurring in
parts of an ecosystem over the time frames of seconds followed by long
periods without ebullition.
Freshwater methane fluxes from streams and rivers were
first estimated to be 1.5 Tg
CH
yr
−1
(Bastviken et al., 2011). However, this study had
measurements from only 21 sites globally. More recently, Stanley et al. (2016) compiled a dataset of 385 sites and estimated a
diffusive emission of 27 Tg
CH
yr
−1
(5th–95th percentiles:
0.01–160 Tg
CH
yr
−1
). Detailed regional studies in the tropics
and temperate watersheds
(Borges et al.,
2015; Campeau and del Giorgio, 2014) support a flux in the range of 27 Tg
CH
yr
−1
as opposed to the initial
1.5 Tg
CH
yr
−1
. However, the low number of measurements, the lack of
clarity on ebullition fluxes, and the large degree of variance in
measurements have precluded an accurate spatial representation of stream and
river methane fluxes. Canals and ditches have recently been highlighted as
high areal emitters (e.g. Stanley et al., 2016), and
their contribution to large-scale emission is typically included in
estimates for overall running waters so far. No new global estimates have
been published since Stanley et al. (2016) and Saunois et al. (2016).
As a result, here we use the same estimate for stream and rivers as in
Saunois et al. (2016):
27 Tg
CH
yr
−1
Methane emissions from lakes were first estimated to be
1–20 Tg
CH
yr
−1
based on measurements in two systems (Great Fresh
Creek, Maryland, and Lake Erie; Ehhalt, 1974). A
subsequent global emission estimate was 11–55 Tg
CH
yr
−1
based on
measurements from three Arctic lakes and a few temperate and tropical
systems (Smith and Lewis, 1992) and 8–48 Tg
CH
yr
−1
using extended data from different latitudes (73 lakes; Bastviken et al., 2004). Based on data from 421 lakes and ponds, Bastviken
et al. (2011) updated their values to 71.6 Tg
CH
yr
−1
, including emissions from non-saline lakes and ponds.
High-latitude lakes have received a large amount of attention in the last
decade. They include both postglacial and thermokarst lakes (small water
bodies formed when peat over melting permafrost collapses), the latter having
larger emissions per square metre but smaller regional emissions than the former
because of their smaller areal extent (Wik et al.,
2016b). Water body depth, sediment type, and ecoclimatic region are the key
factors explaining variation in methane fluxes from lakes
(Wik et al., 2016b). Small artificial water bodies
(ponds) have a high surface-area-to-volume ratio and shallow depth, and they are
likely to be a notable source of methane, at least at the regional scale
(Grinham
et al., 2018; Ollivier et al., 2019). These studies found that emissions
varied by pond type (for example: livestock rearing farm dams vs. cropping
farm dams vs. urban ponds vs. weirs). A rough estimate of the global impact
of this emission source is globally significant, between 3 and 8 Tg
CH
yr
−1
(calculated using the mean emission rates from Grinham et al., 2018, and Ollivier et al., 2019) and an estimate of global
farm impoundment surface area of 77 000 km
(Downing et al.,
2006). This rough estimate does emphasize the potential significance of
these sources, although double-counting with current uncertain estimates
from natural inland water systems is possible
(Thornton et al., 2016a).
A regional estimate for latitudes above 50
north
(Wik et al., 2016b) estimated lake and pond methane
emissions to be 16.5 Tg
CH
yr
−1
(compared to 13.4 Tg
CH
yr
−1
in Bastviken et al. (2011), above 54
N). Tan et al. (2016) used
atmospheric inversion approaches and estimated that the current pan-Arctic
(north of 60
N) lakes emit 2.4–14.2 Tg
CH
yr
−1
, while
a process-based lake biogeochemistry model (bLake4Me) estimated the
emissions at 11.9 [7.1–17.3] Tg
CH
yr
−1
(Tan and Zhuang, 2015). These
numbers for northern or Arctic lakes need to be considered with regard to
the latitudinal area encompassed which differs among studies
(Thornton et al., 2016a). Saunois et al. (2016)
estimates for emissions from natural lakes and ponds were based on those from Bastviken
et al. (2011), using the emissions from the
northern high latitudes above 50
N from Wik et al. (2016b), leading to a rounded mean value of 75 Tg
CH
yr
−1
. Based on the bLake4Me gridded map from Tan and Zhuang (2015), we calculate lake and
pond emissions of 5.2 Tg
CH
yr
−1
above 66
N, close to
the 6.8 Tg
CH
yr
−1
found by Bastviken et
al. (2011). Averaging these two values for the
emissions above 66
N and combining with Bastviken et al. (2011) estimates south of 66
N (64.8 Tg
CH
yr
−1
) leads to a rounded mean global estimate of 71 Tg
CH
yr
−1
close to that of Bastviken et al. (2011)
(71.6 Tg
CH
yr
−1
at the global scale).
On top of emissions pathways described for inland waters,
reservoirs have specific ones including degassing of
CH
from turbines
(hydropower reservoirs only) and elevated diffusive emissions in rivers
downstream of the reservoir – these latter emissions are enhanced if the
water outlet comes from anoxic
CH
-rich hypolimnion waters in the
reservoir
(Bastviken
et al., 2004; Guérin et al., 2006, 2016). In Saunois et al. (2016),
methane emissions from reservoirs were estimated to be 20 Tg
CH
yr
−1
using Bastviken et al. (2011), which was
based on data from 32 systems. A more recent and extensive review estimated
total reservoir emissions to be 18 Tg
CH
yr
−1
(95 % confidence
interval 12–30 Tg
CH
yr
−1
=75
, Deemer et
al., 2016) and is used to revise our estimate in this study.
Combining
emissions from lakes and ponds from Bastviken et al. (2011) (71.6 Tg
CH
yr
−1
) with the recent
estimate of Deemer et al. (2016) for reservoirs and the
streams and river estimates from Stanley et al. (2016) leads
to total inland freshwater emissions of 117 Tg
CH
yr
−1
. Recently,
using a new up-scaling approach based on size weighting productivity and
chlorophyll
, DelSontro et al. (2018)
provided combined lake and reservoir estimates of 104 (5th–95th
percentiles: 67–165), 149 (5th–95th percentiles: 95-236), and 185 (5th–95th
percentiles: 119–295) Tg
CH
yr
−1
, using the lake size
distributions from Downing et al. (2006), Messager et
al. (2016), and Verpooter et al. (2014),
respectively. These estimates are higher (by 10 %, 57 %, and almost
100 %, respectively) than previously reported in Saunois et al. (2016)
(i.e., 95 Tg
CH
yr
−1
for lakes, ponds, and reservoirs).
Previously, Kirschke et al. (2013)
reported a range of 8–73 Tg
CH
yr
−1
for this ensemble of emissions and Saunois et al. (2016)
a mean value of 122 Tg
CH
yr
−1
(75 Tg
CH
yr
−1
for
lakes and ponds, adding 20 Tg
CH
yr
−1
for reservoirs
(Bastviken et al., 2011) and 27 Tg
CH
yr
−1
for streams and rivers; Stanley et al., 2016).
This mean value reported by Saunois et al. (2016)
was based on a single set of estimates, to which a 50 % uncertainty was
associated as a range (60–180 Tg
CH
yr
−1
). Here the new estimates
of DelSontro et al. (2018) lead to a mean
estimate of all inland freshwaters at 159 Tg
CH
yr
−1
associated
with the range 117–212 Tg
CH
yr
−1
that reflects the minimum and
maximum values of the available studies (see Methodology, Sect. 2). However,
it should be noted that this range does not consider the uncertainty of
individual studies. Importantly, these current estimates do not include the
smallest size class of lakes or ephemeral streams, resulting in a possible
misallocation of freshwater fluxes to wetland ecosystems in spite of the
attempts to discount open-water emissions from the wetland estimate (see
above). The present data indicate that lakes or natural ponds, flooded
land–reservoirs, and streams–rivers account for 70 %, 13 %, and 17 % of
the average inland water fluxes, respectively (given the large uncertainty,
the percentages should be seen as approximate relative magnitudes only). The
anthropogenic part of the inland water fluxes is best constrained for larger
reservoirs but remains less clear for other human-made flooded land. It
should be noted that issues regarding spatio-temporal variability are not
considered in consistent ways at present (Natchimuthu et
al., 2015; Wik et al., 2016a). Given the inconsistencies in the areal flux
data and in area estimates, the aim to make frequent updates of the methane
emissions is presently not possible for inland water emissions. Even more
than for other emission categories, differences in inland water flux values
used to estimate emissions, as well as how the data were processed, are more
likely to represent differences between data, rather than reflecting real
temporal trends in the environment.
Figure 4
Methane emissions (mg
CH
−2
−1
) from three
natural sources (left colour scale): geological (Etiope et al., 2019),
termites (this study), and oceans (Weber et al., 2019). Methane uptake in
soils (mg
CH
−2
−1
) presented in positive units (right
colour scale) and based on Murguia-Flores et al. (2018).
The improvement in quantifying inland water fluxes is highly dependent on
the availability of more accurate assessments of their surface area. For
streams and rivers, the 355 000 km
used in Bastviken et al. (2011) was re-evaluated to 540 000 km
by
Stanley et al. (2016) due to a new surface area estimate from
Raymond et al. (2013). Regarding lakes
and reservoirs, the
three current inventories
(Downing
et al., 2006; Messager et al., 2016; Verpoorter et al., 2014) show typical
differences of a factor of 2 to 5 by size class. Also, it was noted that
small ponds, which were not included in either Downing et al. (2006) or
Verpoorter et al. (2014), have a diffusive flux higher
than any other size class of lakes (Holgerson and Raymond,
2016). Further analysis, and possibly more refined process-based models, is
still necessary and urgent to evaluate these global up-scaled estimates
against region-specific approaches such as in Wik et al. (2016a) for the northern high-latitude lakes.
In this budget, we report a mean value of 159 Tg
CH
yr
−1
from
freshwater systems (lakes, ponds, reservoirs, streams, and rivers), with a
range of 117–212 Tg
CH
yr
−1
. This range shows the minimum and
maximum estimates but excludes the uncertainty from each single estimate,
which is expected to be large.
3.2.3
Onshore and offshore geological sources
Significant amounts of methane, produced within the Earth's crust, naturally
migrate to the atmosphere through tectonic faults and fractured rocks. Major
emissions are related to hydrocarbon production in sedimentary basins
(microbial and thermogenic methane), through continuous or episodic
exhalations 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, diffuse microseepage, and geothermal manifestations including
volcanoes. One source is offshore: submarine seepage, which may include the
same types of gas manifestations occurring on land. Etiope et al. (2019) have produced the first gridded maps of geological
methane emissions and their isotopic signature for these five categories,
with a global total of 37.4 Tg
CH
yr
−1
(reproduced in Fig. 4).
According to them, the grid maps do not represent, however, the actual
global geological
CH
emission because the datasets used for the
spatial gridding (developed for modelling purposes) were not complete or did
not contain the information necessary for improving all previous estimates.
Combining the best estimates for the five categories of geological sources
(from grid maps or from previous statistical and process-based models), the
breakdown by category reveals that onshore microseepage dominates (24 Tg
CH
yr
−1
), with the other categories having similar smaller
contributions: as average values, 4.7 Tg
CH
yr
−1
for geothermal
manifestations, about 7 Tg
CH
yr
−1
for submarine seepage, and 9.6 Tg
CH
yr
−1
for onshore seeps and mud volcanoes. These values lead
to a global bottom-up geological emission mean of 45 [27–63] Tg
CH
yr
−1
(Etiope and Schwietzke, 2019).
While all bottom-up and some top-down estimates, following different and
independent techniques from different authors, consistently suggest a global
geo-
CH
emission of the order of 40–50 Tg yr
−1
, the radiocarbon
14
C−CH
) data in ice cores reported by Hmiel et al. (2020) appear to lower the estimate,
with a minimum of about 1.6 Tg
CH
yr
−1
and a maximum estimated
value of 5.4 Tg
CH
yr
−1
(95 %) for the
pre-industrial period. The discrepancy between Hmiel et al. (2020) and all other estimates
continues to feed the debate. Eastern Siberian Arctic Shelf (ESAS) emissions
have been estimated at
3 Tg
CH
yr
−1
based on
current atmospheric surface observations
(Thornton et al., 2020), corresponding
to the same order of magnitude of the estimate from Hmiel et al. (2020) for global geological
emissions.
However, ESAS emissions are likely from both thermogenic and
biogenic origins (e.g. Berchet et al.,
2020). More investigation and confrontation between top-down and bottom-up
results are needed to reduce this discrepancy.
Waiting for further investigation on this topic, we decided to keep the best
estimates from Etiope and Shwietzke (2019) for the mean values
and associate them with the lowest estimates reported in Etiope et al. (2019). Thus, we report a total global geological emission of
45 [18–63] Tg
CH
yr
−1
, with a breakdown between offshore
emissions of 7 [5–10] Tg
CH
yr
−1
and onshore emissions of 38
[13–53] Tg
CH
yr
−1
. The updated bottom-up estimate is slightly
lower than the previous budget mostly due to a reduction of estimated
emissions of onshore and offshore seeps (see Sect. 3.2.6 for more offshore
contribution explanations).
3.2.4
Termites
Termites are an infraorder of insects (Isoptera), which occur predominantly
in the tropical and subtropical latitudes (Abe et al.,
2000).
CH
is released during the anaerobic decomposition of plant
biomass in their gut (Sanderson, 1996). The
uncertainty related to this
CH
source is very high as
CH
emissions from termites in different ecosystem types can vary and are driven
by a range of factors, while the number of field measurements, both of
termite biomass and emissions, is relatively low
(Kirschke et al., 2013).
In Kirschke et al. (2013)
(see their Supplement), a re-analysis of
CH
emissions from
termites at the global scale was proposed. Their
CH
emissions per unit
of area were estimated as the product of termite biomass, termite
CH
emissions per unit of termite mass, and a scalar factor expressing the
effect of land use and cover change; the latter two terms were estimated from
published literature re-analysis. For tropical climates, termite biomass was
estimated by a simple regression model representing its dependence on gross
primary productivity (GPP), whereas for forest and grassland ecosystems of
the warm temperate climates and for shrublands of the Mediterranean
subclimate, termite biomass was estimated from data reported by Sanderson (1996). The
CH
emission factor per unit of
termite biomass (g
termite
) was estimated as 2.8 mg
CH
(g
termite
−1
−1
for tropical ecosystems and Mediterranean
shrublands
(Kirschke
et al., 2013) and 1.7 mg
CH
(g
termite
−1
−1
for
temperate forests and grasslands (Fraser et al., 1986).
Emissions were scaled up and annual
CH
fluxes were computed for the
three periods 1982–1989, 1990–1999, and 2000–2007 representative of the
1980s, 1990s, and 2000s, respectively.
The re-analysis of termite emissions proposed in Saunois et al. (2016)
maintained the same approach, but the data were calculated using climate
zoning (following the Köppen–Geiger classification) applied to updated
climate datasets by Santini and di Paola (2015) and were adapted
to consider different combinations of termite biomass per unit area and
CH
emission factor per unit of termite biomass.
Here, this analysis is extended to cover the periods 2000–2007 and
2010–2016. This latest estimate follows the approach outlined above for
Saunois et al. (2016).
However, in order to extend the analysis to 2016, an alternative MODIS-based
measure of GPP from Zhang at al. (2017), rather
than from Jung et al. (2009), and Jung et al. (2011)
was used to estimate termite biomass. To have coherent datasets of GPP and
land use, the latter variable, previously derived from Ramankutty and Foley (1999), was substituted for MODIS maps
(Channan et al., 2014; Friedl et al., 2010).
These new estimates covered 2000–2007 and 2010–2016 using 2002 and 2012
MODIS data as an average reference year for each period, respectively.
Termite
CH
emissions show only little inter-annual and inter-decadal
variability (0.1 Tg
CH
yr
−1
), whereas there is strong regional
variability, with tropical South America and Africa being the main sources
(23 % and 28 % of the total emissions, respectively) due to the extent of
their natural forest and savannah ecosystems (Fig. 4). Changing the GPP and
land use dataset sources had only a minimal impact on the 2000–2007 global
termite flux, increasing it from 8.7 Tg
CH
yr
−1
as found in the
first two re-analyses (Kirschke
et al., 2013; Saunois et al., 2016) to 9.9 Tg
CH
yr
−1
(present
data), well within the estimated uncertainty (
8.7±3.1
Tg
CH
yr
−1
). However, it had a noticeable effect on the spatial distribution
of the flux (Fig. S2). The most obvious of these changes is a halving of the
Southeast Asian flux, aligned with shifts in the underlying GPP product.
Previous studies (Mercado et al.,
2009; Zhang et al., 2017) had linked these GPP shifts to a methodological
issue with light-use efficiency that drove an underestimation of evergreen
broadleaf and evergreen needleleaf forest GPP, biomes which are prevalent in
the tropics. This value is close to the average estimate derived from
previous up-scaling studies, which report values spanning from 2 to 22 Tg
CH
yr
−1
(Ciais et al.,
2013).
In this study, we report a decadal value of 9 Tg
CH
yr
−1
(range
[3–15] Tg
CH
yr
−1
, Table 3).
3.2.5
Wild animals
Wild ruminants emit methane through the microbial fermentation process
occurring 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
from wild ruminants to be in the
range of 2–6 Tg
CH
yr
−1
. More recently, Pérez-Barbería (2017) lowered this estimate to
1.1–2.7 Tg
CH
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
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).
Based on these findings, the range adopted in this updated methane budget is
2 [1–3] Tg
CH
yr
−1
(Table 3).
3.2.6
Oceanic sources
Oceanic sources comprise coastal ocean and open ocean methane release.
Possible sources of oceanic
CH
include (1) production from marine
(bare and vegetated) sediments or thawing sub-sea permafrost; (2) in situ
production in the water column, especially in the coastal ocean because of
submarine groundwater discharge (USEPA, 2010b); (3) leaks from geological marine seepage (see also Sect. 3.2.3); and (4) emission from the destabilization of marine hydrates. Once at the seabed,
methane can be transported through the water column by diffusion in a
dissolved form (especially in the upwelling zones), or by ebullition (gas
bubbles, e.g. from geological marine seeps), for instance, in shallow waters
of continental shelves. In coastal vegetated habitats methane can also be
transported to the atmosphere through the
aerenchyma
of emergent aquatic plants
(Ramachandran et al., 2004).
The most common biogenic
ocean emission value found in the literature is 10 Tg
CH
yr
−1
(Rhee et al., 2009b). It
appears that most studies rely on the work of Ehhalt (1974), where the value was estimated on the basis
of the measurements done by Swinnerton and co-workers
(Lamontagne et al.,
1973; Swinnerton and Linnenbom, 1967) for the open ocean, combined with
purely speculated emissions from the continental shelf. Based on basin-wide
observations using updated methodologies, three studies found estimates
ranging from 0.2 to 3 Tg
CH
yr
−1
(Bates
et al., 1996; Conrad and Seiler, 1988; Rhee et al., 2009b), associated with
supersaturations of surface waters that are an order of magnitude smaller
than previously estimated, both for the open ocean (saturation anomaly
0.04; see Rhee et
al., 2009a,
Eq. 4) and for the continental shelf (saturation anomaly
0.2). In their synthesis, indirectly referring to the original observations
from Lambert and Schmidt (1993), Wuebbles and Hayhoe (2002) use a value of 5 Tg
CH
yr
−1
. Proposed explanations for discrepancies regarding sea-to-air
methane emissions in the open ocean rely on experimental biases in the
former studies of Swinnerton and Linnenbom (Rhee et al., 2009b). This
may explain why the Bange et al. (1994)
compilation cites a global source of 11–18 Tg
CH
yr
−1
with a
dominant contribution of coastal regions. Here, we report a range of 0–5 Tg
CH
yr
−1
, with a mean value of 2 Tg
CH
yr
−1
for
biogenic emissions from open and coastal ocean (excluding estuaries).
Biogenic emissions from brackish waters (estuaries, coastal wetlands) were
not reported in the previous budget
(Saunois
et al., 2016). Methane emissions from estuaries were originally estimated by
Bange et al. (1994), Upstill-Goddard et al. (2000), and
Middelburg et al. (2002) to be
comprised between 1 and
−3
Tg
CH
yr
−1
. This range was later
revised upwards by Borges and Abril (2011) to about 7 Tg
CH
yr
−1
based on a methodology distinguishing between different
estuarine types and accounting for the contribution of tidal flats, marshes,
and mangroves, for a total of 39 systems and a global “inner” estuarine
surface area of
1.1×10
km
(Laruelle et al., 2013). The same
methodology as in Laruelle et al. (2013) has been applied here to the
same systems using an expanded database of local and regional measurements
(72 systems) and suggests however that global estuarine
CH
emissions
were overestimated and may actually not surpass 3–3.5 Tg
CH
yr
−1
Despite this overall reduction, the specific contribution of sediment and
water emissions from mangrove ecosystems is however higher and contributes
0.1 to 1.7 Tg
CH
yr
−1
globally
(Rosentreter et al., 2018). This estuarine
estimate does not include the uncertain contribution from large river plumes
protruding onto the shelves. Their surface area reaches about
3.7×10
km
(Kang et al., 2013) but because
of significantly lower
CH
concentration
(e.g.
Osudar et al., 2015; Zhang et al., 2008) than in inner estuaries, the
outgassing associated with these plumes likely does not exceed 1–2 Tg
CH
yr
−1
. Seagrass meadows are also not included, although they
might release
0.1 to 2.7 Tg
CH
yr
−1
(Garcias-Bonet and Duarte, 2017). These
methane emissions from vegetated coastal ecosystems can partially offset
(Rosentreter et al., 2018) their “blue
carbon” sink (e.g.
Mcleod et al., 2011; Nellemann et al., 2009). Note that the latter two
contributions might partly overlap with oceanic (open and coastal) sources
estimates. The total (inner and outer) estuarine emission flux, which is
based on only about 80 systems is thus in the range 4–5 Tg
CH
yr
−1
(including marshes and mangrove). High uncertainties in coastal
ocean emission estimates can be reduced by better defining the various
coastal ecosystem types and their boundaries to avoid double-counting (e.g.
estuaries, brackish wetlands, freshwater wetlands), updating the surface
area of each of these coastal systems, and better quantifying methane
emission rates in each ecosystem type.
As a result, here we report a range of 4–10 Tg
CH
yr
−1
for
emissions from coastal and open ocean (including estuaries), with a mean
value of 6 Tg
CH
yr
−1
The production of methane at the seabed is known to be
significant. For instance, marine seepages emit up to 65 Tg
CH
yr
−1
globally at seabed level (USEPA, 2010b). What
is uncertain is the flux of oceanic methane reaching the atmosphere. For
example, bubble plumes of
CH
from the seabed have been observed in the
water column, but not detected in the Arctic atmosphere
(Fisher
et al., 2011; Westbrook et al., 2009). There are several barriers preventing
methane from being expelled to the atmosphere (James et
al., 2016). From below the seafloor to the sea surface, gas hydrates and
permafrost serve as a barrier to fluid and gas migration towards the
seafloor; microbial activity around the seafloor can strongly oxidize
methane releases or production;
further oxidation occurs in the water
column; the oceanic pycnocline acts as a physical barrier towards the
surface waters, including efficient dissolution of bubbles; and finally,
surface oceans are aerobic and contribute to the oxidation of dissolved
methane. However, surface waters can be more supersaturated than the
underlying deeper waters, leading to a methane paradox (Sasakawa
et al., 2008). Possible explanations involve (i) upwelling in areas with
surface mixed layers covered by sea ice (Damm et al., 2015),
(ii) the release of methane by the degradation of dissolved organic matter
phosphonates in aerobic conditions (Repeta et al.,
2016), (iii)  methane production by marine algae
(Lenhart et al., 2016), or (iv) methane
production within the anoxic centre of sinking particles
(Sasakawa et al., 2008), but more work is still needed to be
conclusive about this apparent paradox.
For geological emissions, the most used value has long been 20 Tg
CH
yr
−1
, relying on expert knowledge and literature synthesis proposed in
a workshop reported in Kvenvolden et al. (2001); the authors of
this study recognize that this was a first estimation and needs revision.
Since then, oceanographic campaigns have been organized, especially to
sample bubbling areas of active seafloor gas seep bubbling. For instance,
Shakhova et al. (2010, 2014) infer 8–17 Tg
CH
yr
−1
in emissions just for the Eastern Siberian Arctic Shelf (ESAS),
based on the extrapolation of numerous but local measurements, and possibly
related to thawing sub-seabed permafrost (Shakhova et
al., 2015). Because of the highly heterogeneous distribution of dissolved
CH
in coastal regions, where bubbles can most easily reach the
atmosphere, extrapolation of in situ local measurements to the global scale
can be hazardous and lead to biased global estimates. Indeed, using very
precise and accurate continuous land shore-based atmospheric methane
observations in the Arctic region, Berchet et al. (2016) found a range of emissions for ESAS
of
2.5 Tg
CH
yr
−1
(range [0–5]), 4–8 times lower
than Shakhova's estimates. Such a reduction in ESAS emission estimate has
also been inferred from oceanic observations by Thornton et al. (2016b) with a maximum sea–air
CH
flux of 2.9 Tg
CH
yr
−1
for this region. Etiope et al. (2019)
suggested a minimum global total submarine seepage emission of 3.9 Tg
CH
yr
−1
simply summing published regional emission estimates for
15 areas for identified emission areas (above 7 Tg
CH
yr
−1
when
extrapolated to include non-measured areas). These recent results, based on
different approaches, suggest that the current estimate of 20 Tg
CH
yr
−1
is too large and needs revision.
Therefore, as discussed in Sect. 3.2.2, we report here a reduced range of
5–10 Tg
CH
yr
−1
for marine geological emissions compared to the
previous budget, with a mean value of 7 Tg
CH
yr
−1
Among the different origins of oceanic methane, hydrates
have attracted a lot of attention. Methane hydrates (or clathrates) are
ice-like crystals formed under specific temperature and pressure conditions
(Milkov, 2005). Methane hydrates can be either of biogenic origin
(formed in situ at depth in the sediment by microbial activity) or of
thermogenic origin (non-biogenic gas migrated from deeper sediments and
trapped due to pressure–temperature conditions or due to some capping
geological structure such as marine permafrost). The total stock of marine
methane hydrates is large but uncertain, with global estimates ranging from
hundreds to thousands of Pg
CH
(Klauda and Sandler,
2005; Wallmann et al., 2012).
Concerning more specifically atmospheric emissions from marine hydrates,
Etiope (2015) points out that current estimates of methane air–sea
flux from hydrates (2–10 Tg
CH
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
CH
has been released in the
water column over 100 years (Kretschmer et al., 2015). Those
few teragrams per year become negligible once consumption in the water column has
been accounted for. While events such as submarine slumps may trigger local
releases of considerable amounts of methane from hydrates that may reach the
atmosphere (Etiope, 2015; Paull et al., 2002),
on a global scale, present-day atmospheric methane 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
CH
yr
−1
emissions.
Combination (biogenic and geological) of open and coastal oceanic emissions.
Summing biogenic, geological and hydrate emissions from open and coastal
ocean (excluding estuaries) leads to a total of 9 Tg
CH
yr
−1
(range
5–17). A recent work (Weber et al., 2019) suggests a new
robust estimate of the climatological oceanic flux: the diffusive flux was
estimated as 2–6 Tg
CH
yr
−1
and the ebullitive flux as 2–11 Tg
CH
yr
−1
, giving a total (open and coastal) oceanic flux estimate
of 6–15 Tg
CH
yr
−1
(90 % confidence interval) when the
probability distributions for the two pathways are combined. Distribution of
open and coastal oceanic fluxes from Weber et al. (2019) is
shown in Fig. 4. This more robust estimate took benefit from synthesis of in
situ measurements of atmospheric and surface water methane concentrations
and of bubbling areas, and of the development of process-based models for
oceanic methane emissions. Another recent estimate based on the
biogeochemistry model PlankTOM10
(Le Quéré et al., 2016)
calculates an open and coastal ocean methane flux (excluding estuaries) of 8
−13
19] Tg
CH
yr
−1
(Buitenhuis et al., 2020),
with a coastal contribution of 44 %. Our estimate (9 [5–17] Tg
CH
yr
−1
) agrees well with the estimates of 6–15 Tg
CH
yr
−1
by
Weber et al. (2019) and 8 Tg
CH
yr
−1
(Buitenhuis et al., 2020).
Methane emissions from brackish water were not estimated in Saunois et al. (2016)
and an additional 4 Tg
CH
yr
−1
is reported in this budget. As a
result, including estuaries in the oceanic budget, we report a range of 9–22 Tg
CH
yr
−1
, with a mean value of 13 Tg
CH
yr
−1
leading to similar total oceanic emissions despite a reduced estimate in
geological offshore emissions compared to Saunois et al. (2016).
3.2.7
Terrestrial permafrost and hydrates
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 million km
or 15 %
of the exposed land surface (Obu et al., 2019). As the
climate warms, large areas of permafrost are also warming, and if soil
temperatures pass 0
C, thawing of the permafrost occurs.
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
or as more rapid localized thaw associated with loss of massive ground ice
(thermokarst) (Schuur et al., 2015).
A total of
1035±150
Pg of carbon can be found in the upper 3 m
of permafrost regions, or
1300 (1100–1500) Pg C for all
permafrost (Hugelius et al., 2014).
The thawing permafrost can generate direct and indirect methane emissions.
Direct methane emissions rely on the release of methane contained in the
thawing permafrost. This flux to the atmosphere is small and estimated to be
a maximum of 1 Tg
CH
yr
−1
at present (USEPA,
2010b). Indirect methane emissions are probably more important. They rely
on (1) methanogenesis induced when the organic matter contained in thawing
permafrost is released, (2) the associated changes in land surface hydrology
possibly enhancing methane production (McCalley et al.,
2014), and (3) the formation of more thermokarst lakes from erosion and soil
collapse. Such methane 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., 2015). However,
indirect methane emissions from permafrost thawing are difficult to estimate
at present, with very few data to refer to, and in any case they largely overlap
with wetland and freshwater emissions occurring above or around thawing
areas. For instance, based on lake and soil
measurements Walter
Anthony et al. (2016) found that methane emissions (
4 Tg
CH
yr
−1
) from thermokarst lakes that have expanded over the past
60 years were directly proportional to the mass of soil carbon inputs to the
lakes from the erosion of thawing permafrost.
Here, we choose to report only the direct emission range of 0–1 Tg
CH
yr
−1
, 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 methane by living
vegetation are considered here (see Covey and Megonigal, 2019, for an extensive review). Firstly,
plants produce methane through an abiotic photochemical process induced by
stress (Keppler et al., 2006). This pathway was initially
criticized
(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-field however,
and although the potential implication for the global methane 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, plants act as
“straws”, drawing up and releasing microbially produced methane from
anoxic soils (Cicerone and Shetter, 1981; Rice
et al., 2010). For instance, in the forested wetlands of Amazonia, tree
stems are the dominant ecosystem flux pathway for soil-produced methane;
therefore, including stem emissions in ecosystem budgets can reconcile
regional bottom-up and top-down estimates
(Pangala et al., 2017). 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 (Pangala et al., 2013, 2015) and tree-stem-based methanogens (Pitz and
Megonigal, 2017; Wang et al., 2016). A recent synthesis indicates stem
CH
emissions significantly increase the source strength of forested
wetlands and modestly decrease the sink strength of upland forests (Covey and Megonigal, 2019). The scientific
activity covering
CH
emissions in forested ecosystems reveals a far
more complex story than previously thought, with an interplay of,
productive–consumptive, aerobic–anaerobic, biotic–abiotic processes
occurring between upland–wetland soils, trees, and atmosphere. Understanding
the complex processes that regulate
CH
source–sink dynamics in
forests and estimating their contribution to the global methane 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 recognize 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 ascribed here to other flux category sources (e.g. inland
waters, wetlands, upland soils).
3.3
Methane sinks and lifetime
Methane 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 methane (
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, O(
D)), oxidation in soils
(Curry, 2007; Dutaur and Verchot, 2007), and
photochemistry in the marine boundary layer (reaction with Cl; Allan et al., 2007;
Thornton et al., 2010). Uncertainties in the total sink
of methane as estimated by atmospheric chemistry models are on the order 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 methane budget, we
estimate bottom-up methane chemical sinks and lifetime mainly based on
global model results from the Chemistry Climate Model Initiative (CCMI)
(Morgenstern et al., 2017).
3.3.1
Tropospheric OH oxidation
OH radicals are produced following the photolysis of ozone (
) in the
presence of water vapour. OH is destroyed by reactions with CO,
CH
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)
(Morgenstern et al., 2017).
Mass-weighted OH tropospheric concentrations do not directly represent
methane loss, as the spatial and vertical distributions of OH affect this
loss, through, in particular, the temperature dependency and the
distribution of methane
(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 multi-model mean (11 models) global
mass-weighted OH tropospheric concentration was
11.7
1.0
10
molecules cm
−3
(range 9.9–14.4
10
molecules cm
−3
Zhao et al., 2019)
consistent with the previous estimates from ACCMIP (
11.7
1.0
10
molecules cm
−3
, with a range of 10.3–13.4
10
molecules cm
−3
, Voulgarakis et al., 2013, for the year 2000)
and the estimates of Prather et al. (2012) at
11.2
1.3
10
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 ozone, and modelled ozone and
carbon monoxide. 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 leading to a NH
SH OH
ratio greater than 1
(Naik
et al., 2013; Zhao et al., 2019). However, there is evidence for parity in
inter-hemispheric OH concentrations
(Patra
et al., 2014), which needs to be confirmed by other observational and
model-derived estimates.
OH concentrations and their changes can be sensitive to climate variability
(Dlugokencky
et al., 1996; Holmes et al., 2013; Turner et al., 2018), biomass burning
(Voulgarakis et al., 2015) and anthropogenic activities. For
instance, the increase in the oxidizing capacity of the troposphere in South
and East Asia associated with increasing
NO
emissions
(Mijling et al., 2013) and decreasing CO emissions
(Yin et al., 2015) possibly enhances
CH
oxidation
and therefore limits the atmospheric impact of increasing emissions
(Dalsøren et al., 2009). Despite such large
regional changes, the global mean OH concentration was suggested to have
changed only slightly over the past 150 years
(Naik et al.,
2013). This is due to the compensating effects of the concurrent increases
in positive influences on OH (water vapour, tropospheric ozone, nitrogen
oxides (
NO
) emissions, and UV radiation due to decreasing stratospheric
ozone) and of OH sinks (methane burden, carbon monoxide and non-methane
volatile organic compound emissions and burden). CCMI models show OH
inter-annual variability ranging from 0.4 % to 1.8 %
(Zhao et al.,
2019) over 2000–2010, lower than the value deduced from methyl chloroform
measurements (proxy, top-down approach). However, these simulations consider
meteorology variability but not emission inter-annual variability (e.g., from
biomass burning) and thus are expected to simulate lower OH inter-annual
variability than in reality. Using an empirical model constrained by global
observations of ozone, water vapour, methane, and temperature as well as the
simulated effects of changing
NO
emissions and tropical expansion, Nicely
et al. (2017)
found an inter-annual variability in OH of about 1.3 %–1.6 % between 1980
and 2015, in agreement with the methyl chloroform proxy
(Montzka et al., 2011).
We report here a climatological range for the tropospheric loss of methane
by OH oxidation of 553 [476–677] Tg
CH
yr
−1
derived from the
seven models that contributed to CCMI for the total tropospheric loss of
methane by OH oxidation over the period 2000–2009 (tropopause height at 200 hPa), which is slightly higher than the one from the ACCMIP models (528
[454–617] Tg
CH
yr
−1
reported in Kirschke et al. (2013)
and Saunois et al. (2016).
3.3.2
Stratospheric loss
In the stratosphere,
CH
is lost through reactions with excited atomic
oxygen O(
D), atomic chlorine (Cl), atomic fluorine (F), and OH
(Brasseur and Solomon, 2005;
le Texier et al., 1988). Uncertainties in the chemical loss of stratospheric
methane are large, due to uncertain inter-annual variability in
stratospheric transport as well as its chemical interactions and feedbacks
with stratospheric ozone (Portmann et
al., 2012). In particular, the fraction of stratospheric loss
due to the
different oxidants is still uncertain, with possibly 20 %–35 % due to
halons, about 25 % due to O(
D) mostly in the high stratosphere, and
the rest due to stratospheric OH
(McCarthy et al., 2003).
In this study, seven chemistry–climate models from the CCMI project (Table S4) are used to provide estimates of methane chemical loss, including
reactions with OH, O(
D), and Cl;
CH
photolysis is also included
but occurs only above the stratosphere. Considering a 200 hPa tropopause
height, the CCMI models suggest an estimate of 31 [12–37] Tg
CH
yr
−1
for the methane stratospheric sink for the period 2000–2010 (Table S4). The 20 Tg difference compared to the mean value reported by Kirschke et
al. (2013)
and Saunois et al. (2016)
for the same period (51 [16–84] Tg
CH
yr
−1
) is probably due to
the plausible double-counting of O(
D) and Cl oxidations in our
previous calculation, as the chemistry–climate models usually report the
total chemical loss of methane (not OH oxidation only).
We report here a climatological range of 12–37 Tg
CH
yr
−1
associated with a mean value of 31 Tg
CH
yr
−1
3.3.3
Tropospheric reaction with Cl
Halogen atoms can also contribute to the oxidation of methane in the
troposphere. Allan et al. (2005) measured mixing
ratios of methane and
13
C−CH
at two stations in the
Southern Hemisphere from 1991 to 2003, and they found that the apparent kinetic
isotope effect (KIE) of the atmospheric methane sink was significantly
larger than that explained by OH alone. A seasonally varying sink due to
atomic chlorine (Cl) in the marine boundary layer of between 13 and 37 Tg
CH
yr
−1
was proposed as the explanatory mechanism
(Allan
et al., 2007; Platt et al., 2004). This sink was estimated to occur mainly
over coastal and marine regions, where NaCl from evaporated droplets of
seawater reacts with
NO
to eventually form
Cl
, which then
UV-dissociates to Cl. However significant production of nitryl chloride
ClNO
) at continental sites has been recently reported
(Riedel et al., 2014) and
suggests the broader presence of Cl, which in turn would expand the
significance of the Cl sink in the troposphere. Recently, using a chemistry-transport model, Hossaini et al. (2016) suggest a chlorine
sink in the lower range of Allan et al. (2007),
12–13 Tg
CH
yr
−1
(about 2.5 % of the
tropospheric sink). They also estimate that
ClNO
yields a 1 Tg yr
−1
sink of methane. Another modelling study
(Wang et al.,
2019b) produced a more comprehensive analysis of global tropospheric
chlorine chemistry and found a chlorine sink of 5 Tg yr
−1
, representing
only 1 % of the total methane tropospheric sink. Both the KIE approach and
chemistry-transport model simulations carry uncertainties (extrapolations
based on only a few sites and use of indirect measurements, for the former;
missing sources, coarse resolution, underestimation of some anthropogenic
sources for the latter). However, Gromov et al. (2018) found that chlorine can contribute only
0.23 % to the tropospheric sink of methane (about 1 Tg
CH
yr
−1
) in
order to balance the global
13
C(CO)
budget.
Awaiting further work to better assess the magnitude of the chlorine sink in
the methane budget, we suggest a lower estimate but a larger range than in
Saunois et al. (2016)
and used the following climatological value for the 2000s: 11 [1–35] Tg
CH
yr
−1
3.3.4
Soil uptake
Unsaturated oxic soils are sinks of atmospheric methane due to the presence
of methanotrophic bacteria, which consume methane as a source of energy.
Dutaur and Verchot (2007) conducted a comprehensive
meta-analysis of field measurements of
CH
uptake spanning a variety of
ecosystems. Extrapolating to the global scale, they reported a range of
36±23
Tg
CH
yr
−1
, but they also showed that stratifying the
results by climatic zone, ecosystem, and soil type led to a narrower range
(and lower mean estimate) of
22±12
Tg
CH
yr
−1
. Modelling
studies, employing meteorological data as external forcing, have also
produced a considerable range of estimates. Using a soil depth-averaged
formulation based on Fick's law with parameterizations for diffusion and
biological oxidation of
CH
, Ridgwell et al. (1999)
estimated the global sink strength at 38 Tg
CH
yr
−1
, with a range of
20–51 Tg
CH
yr
−1
reflecting the model structural uncertainty in
the base oxidation parameter. Curry (2007) improved
on the latter by employing an exact solution of the one-dimensional
diffusion-reaction equation in the near-surface soil layer (i.e.,
exponential decrease in
CH
concentration below the surface), a land
surface hydrology model, and calibration of the oxidation rate to field
measurements. This resulted in a global estimate of 28 Tg
CH
yr
−1
(9–47 Tg
CH
yr
−1
), the result reported by Zhuang et al. (2013), Kirschke et al. (2013),
and Saunois et al. (2016).
Ito and Inatomi (2012) used an ensemble methodology to explore
the variation in estimates produced by these parameterizations and others,
which spanned the range 25–35 Tg
CH
yr
−1
. Murguia-Flores et al. (2018)
further refined the Curry (2007) model's structural
and parametric representations of key drivers of soil methanotrophy,
demonstrating good agreement with the observed latitudinal distribution of
soil uptake (Dutaur and Verchot, 2007). Their model simulated a
methane soil sink of 32 Tg
CH
yr
−1
for the period 2000–2017 (Fig. 4), compared to 38 and 29 Tg
CH
yr
−1
using the Ridgwell et al. (1999) and Curry (2007)
parameterizations, respectively, under the same meteorological forcing. As
part of a more comprehensive model accounting for a range of methane sources
and sinks, Tian et al. (2010, 2015, 2016)
computed vertically averaged
CH
soil uptake including the additional
mechanisms of aqueous diffusion and plant-mediated (
aerenchyma
) transport, arriving at
the estimate
30±19
Tg
CH
yr
−1
(Tian et al., 2016). The still more
comprehensive biogeochemical model of Riley et al.
(2011)
included vertically resolved representations of the same processes
considered by Tian et al. (2016), in
addition to grid cell fractional inundation and, importantly, the joint
limitation of uptake by both
CH
and O
availability in the soil
column. Riley et al. (2011)
estimated a global
CH
soil sink of 31 Tg
CH
yr
−1
with a
structural uncertainty of 15–38 Tg
CH
yr
−1
(a higher upper limit
resulted from an elevated gas diffusivity to mimic convective transport; as
this is not usually considered, we adopt the lower upper bound associated
with no limitation of uptake at low soil moisture). A model of this degree
of complexity is required to explicitly simulate situations where the soil
water content increases enough to inhibit the diffusion of oxygen, and the
soil becomes a methane source (Lohila et al., 2016). This
transition can be rapid, thus creating areas (for example, seasonal
wetlands) that can be either a source or a sink of methane depending on the
season.
The previous Curry (2007) estimate can be revised
upward based on subsequent work and the increase in
CH
concentration
since that time, which gives a central estimate of 30.1 Tg
CH
yr
−1
. Considering structural uncertainty in the various models'
assumptions and parameters, we report here the median and range of Tian et
al. (2016): 30 [11–49] Tg
CH
yr
−1
for the periods 2000–2009 and
2008–2017.
3.3.5
CH
lifetime
The atmospheric lifetime of a given gas in steady state may be defined as
the global atmospheric burden (Tg) divided by the total sink (Tg yr
−1
(IPCC, 2001). Global models provide an estimate of the
loss of the gas due to individual sinks, which can then be used to derive
lifetime due to a specific sink. For example, methane's tropospheric
lifetime is determined as global atmospheric methane burden divided by the
loss from OH oxidation in the troposphere, sometimes called “chemical
lifetime”. Methane total lifetime corresponds to the global burden divided
by the total loss including tropospheric loss from OH oxidation,
stratospheric chemistry, and soil uptake. The CCMI models (described in
Morgenstein et al., 2017) estimate the
tropospheric methane lifetime at about 9 years (average over years
2000–2009), with a range of 7.2–10.1 years (see Table S4). While this range
agrees with previous values found in ACCMIP (9.3 [7.1–10.6] years;
Voulgarakis et al., 2013), the mean value
reported here is lower than previously reported, probably due to a smaller
and different ensemble of climate models. Adding 30 Tg to account for the
soil uptake to the total chemical loss of the CCMI models, we derive a total
methane lifetime of 7.8 years (average over 2000–2009 with a range of
6.5–8.8 years). These updated model estimates of total methane lifetime
agree with the previous estimates from ACCMIP (8.2 [6.4–9.2] years for the year
2000; Voulgarakis et al., 2013). Reducing the
large spread in methane lifetime (between models and between models and
observation-based estimates) would (1) bring an improved constraint on global
total methane emissions and (2) ensure an accurate forecast of future
climate.
Atmospheric observations and top-down inversions
4.1
Atmospheric observations
Systematic atmospheric
CH
observations began in 1978
(Blake et al., 1982) with infrequent measurements from
discrete air samples collected in the Pacific at a range of latitudes from
67
N to 53
S. Because most of these air samples were
from well-mixed oceanic air masses and the measurement technique was precise
and accurate, they were sufficient to establish an increasing trend and the
first indication of the latitudinal gradient of methane. Spatial and
temporal coverage was greatly improved soon after (Blake and
Rowland, 1986) with the addition of the Earth System Research Laboratory
from US National Oceanic and Atmospheric Administration (NOAA/ESRL) flask
network (Steele et al., 1987, Fig. 1) and of
the Advanced Global Atmospheric Gases Experiment (AGAGE)
(Cunnold
et al., 2002; Prinn et al., 2000), the Commonwealth Scientific and
Industrial Research Organisation (CSIRO; Francey et al., 1999), the University of California Irvine (UCI;
Simpson et al., 2012), and in situ and flask measurements from
regional networks, such as the ICOS (Integrated Carbon Observation System)
network in Europe (INGOS, 2018; ICOS-RI, 2019;
, last access: 29 June 2020). The combined
datasets provide the longest time series of globally averaged
CH
abundance. Since the early 2000s,
CH
column-averaged mole fractions
have been retrieved through passive remote sensing from space (Buchwitz
et al., 2005a, b; Butz et al., 2011; Crevoisier et al., 2009;
Frankenberg et al., 2005; Hu et al., 2018). Ground-based Fourier transform
infrared (FTIR) measurements at fixed locations also provide time-resolved
methane column observations during daylight hours and a validation dataset
against which to evaluate the satellite measurements such as the TCCON network
(e.g.
Pollard et al., 2017; Wunch et al., 2011) or the Network for Detection of
Atmospheric Composition Change (NDACC)
(e.g. Bader et al., 2017).
In this budget, in situ observations from the different networks were used
in the top-down atmospheric inversions to estimate methane sources and sinks
over the period 2000–2017. Satellite observations from the TANSO/FTS instrument
on board the satellite GOSAT were used to estimate methane sources and sinks
over the period 2009–2017. Other atmospheric data (FTIR, airborne
measurements, AirCore, isotopic measurements, etc.) have been used
for validation by some groups but not specifically in this study. However,
further information is provided in the Supplement, and a more
comprehensive validation of the inversions is planned to use some of these
data.
4.1.1
In situ
CH
observations and atmospheric growth rate at the surface
We use globally averaged
CH
mole fractions at the Earth's surface from
the four observational networks (NOAA/ESRL, AGAGE, CSIRO, and UCI). The data
are archived at the World Data Centre for Greenhouse Gases (WDCGG) of the
WMO Global Atmospheric Watch (WMO GAW) programme, including measurements from
other sites that are not operated as part of the four networks. The
CH
in situ monitoring network has grown significantly over the last decade
due to the emergence of laser diode spectrometers which are robust and
accurate enough to allow deployments with minimal maintenance, enabling the
development of denser networks in developed countries
(Stanley
et al., 2018; Yver Kwok et al., 2015) and new stations in remote
environments (Bian
et al., 2015; Nisbet et al., 2019).
The networks differ in their sampling strategies, including the frequency of
observations, spatial distribution, and methods of calculating globally
averaged
CH
mole fractions. Details are given in the Supplement of Kirschke et al. (2013).
The global average values of
CH
concentrations presented in Fig. 1 are
computed using long time series measurements through gas chromatography with
flame ionization detection (GC–FID), although chromatographic schemes vary
among the labs. Because GC–FID is a relative measurement method, the
instrument response must be calibrated against standards. The current WMO
reference scale, maintained by NOAA/ESRL, WMO-X2004A
(Dlugokencky et al., 2005), was updated in
July 2015. NOAA and CSIRO global means are on this scale. AGAGE uses an
independent standard scale maintained by Tohoku University
(Aoki et al., 1992), but direct comparisons of
standards and indirect comparisons of atmospheric measurements show that
differences are below 5 ppb (Tans
and Zwellberg, 2014; Vardag et al., 2014). UCI uses another independent
scale that was established in 1978 and is traceable to NIST
(Flores et al.,
2015; Simpson et al., 2012) but has not been included in standard exchanges
with other networks so differences with the other networks cannot be
quantitatively defined. Additional experimental details are presented in the
supplementary material from Kirschke et al. (2013)
and references therein.
In Fig. 1, (a) globally averaged
CH
and (b) its growth rate
(derivative of the deseasonalized trend curve) through to 2017 are plotted
for the four measurement programmes using a procedure of signal decomposition
described in Thoning et al. (1989). We define the annual
ATM
as the increase in the atmospheric concentrations from 1 January in
one year to 1 January in the next year. Agreement among the four networks is
good for the global growth rate, especially since
1990. The
large differences observed mainly before 1990 probably reflect the different
spatial coverage of each network. The long-term behaviour of globally
averaged atmospheric
CH
shows a decreasing but positive growth rate
(defined as the derivative of the deseasonalized mixing ratio) from the
early 1980s through 1998, a near stabilization of
CH
concentrations
from 1999 to 2006, and a renewed period with positive persistent growth
rates since 2007, slightly larger after 2014. When a constant atmospheric
lifetime is assumed, the decreasing growth rate from 1983 through 2006 may
imply that atmospheric
CH
was approaching steady state, with no trend
in emissions. The NOAA global mean
CH
concentration was fitted with a
function that describes the approach to a first-order steady state (ss
index):
CH
CH
ss
CH
ss
CH
solving for the lifetime,
, gives 9.3 years, which is very close to
current literature values (e.g. Prather et al., 2012,
9.1±0.9
years). Such an
approach includes uncertainties, especially due to the strong assumption of
no trend in emissions and sinks, which does not agree with some studies
explaining the stabilization period by decreasing emissions associated with
increasing sink
(e.g. Bousquet
et al., 2006). However, this value seems consistent albeit higher than the
chemistry–climate estimates (8.2  years; see Sect. 3.3.5)
From 1999 to 2006, the annual increase in atmospheric
CH
was
remarkably small at
0.6±0.1
ppb yr
−1
. Since 2006, the
atmospheric growth rate has recovered to a level similar to that of the
mid-1990s (
5 ppb yr
−1
), or even to that of the 1980s
for 2014 and 2015 (
10 ppb yr
−1
). On decadal timescales,
the annual increase is on average
2.1±0.3
ppb yr
−1
for
2000–2009,
6.6±0.3
ppb yr
−1
for 2008–2017, and
6.1±1.0
ppb
yr
−1
for the year 2017.
4.1.2
Satellite data of column-average
CH
In this budget, we use satellite data from the JAXA satellite Greenhouse
Gases Observing SATellite (GOSAT) launched in January 2009 (Butz
et al., 2011; Morino et al., 2011) containing the TANSO-FTS instrument,
which observes in the shortwave infrared (SWIR). Different retrievals of
methane based on TANSO-FTS GOSAT products are made available to the
community: from NIES (Yoshida
et al., 2013), from SRON
(Schepers et al., 2012),
and from the University of Leicester (Parker et al., 2011). The
three retrievals are used by the top-down systems (Tables 4 and S6). Although
GOSAT retrievals still show significant unexplained biases and limited
sampling in cloud-covered regions and in the high-latitude winter, it
represents an important improvement compared to the first satellite
measuring methane from space, SCIAMACHY (Scanning Imaging Absorption
spectrometer for Atmospheric CartograpHY) for both random and systematic
observation errors (see Table S2 of Buchwitz et al., 2017).
Atmospheric inversions based on SCIAMACHY and GOSAT
CH
retrievals were
reported in Saunois et al. (2017).
Here, only inversions using GOSAT retrievals are used.
4.2
Top-down inversions used in the budget
An atmospheric inversion is the optimal combination of atmospheric
observations, of a model of atmospheric transport and chemistry, of a prior
estimate of methane sources and sinks, and of their uncertainties, in order
to provide improved estimates of the sources and sinks, and their
uncertainty. The theoretical principle of methane inversions is detailed in
the Supplement (Sect. S2) and an overview of the different methods
applied to methane is presented in Houweling et al. (2017).
Table 4
Top-down studies used in our analysis, with their contribution
to the decadal and yearly estimates noted. For decadal means, top-down
studies have to provide at least 8 years of data over the decade to
contribute to the estimate.
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We consider here an ensemble of inversions gathering various chemistry-transport models, differing in vertical and horizontal resolutions,
meteorological forcing, advection and convection schemes, and boundary layer
mixing. Including these different systems is a conservative approach that
allows us to cover different potential uncertainties of the inversion, among
them model transport, set-up issues, and prior dependency. General
characteristics of the inversion systems are provided in Table 4. Further
details can be found in the referenced papers and in the Supplement. Each group was asked to provide gridded flux estimates for the
period 2000–2017, using either surface or satellite data, but no additional
constraints were imposed so that each group could use their preferred
inversion set-up. A set of prior emission distributions was built from the
most recent inventories or model-based estimates (see Supplement), but its use was not mandatory (Table S6). This approach
corresponds to a flux assessment but not to a model inter-comparison as the
protocol was not too stringent. Estimating posterior uncertainty is time and
computer resource consuming, especially for the 4D-Var approaches and Monte
Carlo methods. Posterior uncertainties have been provided by only two groups
and are found to be lower than the ensemble spread. Indeed, chemistry-transport models differ in inter-hemispheric transport, stratospheric
methane profiles, and OH distribution, which are not fully
considered in the individual posterior uncertainty. As a result, we do not
use the posterior uncertainties provided by these two groups but report the
minimum–maximum range among the different top-down approaches.
Nine atmospheric inversion systems using global Eulerian transport models
were used in this study compared to eight in Saunois et al. (2016).
Each inversion system provided one or several simulations, including
sensitivity tests varying the assimilated observations (surface or
satellite) or the inversion set-up. This represents a total of 22 inversion
runs with different time coverage: generally 2000–2017 for surface-based
observations and 2010–2017 for GOSAT-based inversions (Tables 4 and S6). In poorly observed regions, top-down surface inversions may rely on the
prior estimates and bring little or no additional information to constrain
(often) spatially overlapping emissions (e.g. in India, China). Also, we
recall that many top-down systems solve for the total fluxes at the surface
only or for some categories that may differ from the GCP categories. When
multiple sensitivity tests were performed the mean of this ensemble was used
not to over-weight one particular inverse system. It should also be noticed
that some satellite-based inversions are in fact combined satellite and
surface inversions as they use satellite retrievals and surface measurements
simultaneously
(Alexe
et al., 2015; Bergamaschi et al., 2013; Houweling et al., 2014).
Nevertheless, these inversions are still referred to as satellite-based
inversions.
Each group provided gridded monthly maps of emissions for both their prior
and posterior total and for sources per category (see the categories Sect. 2.3). Results are reported in Sect. 5. Atmospheric sinks from the top-down
approaches have been provided for this budget and are compared with the
values reported in Kirschke et al. (2013).
Not all inverse systems report their chemical sink; as a result, the global
mass imbalance for the top-down budget is derived as the difference between
total sources and total sinks for each model when both fluxes were reported.
Methane budget: top-down and bottom-up comparison
5.1
Global methane budget
5.1.1
Global total methane emissions
At the global scale, the total emissions inferred by the
ensemble of 22 inversions is 576 Tg
CH
yr
−1
[550–594] for the
2008–2017 decade (Table 3), with the highest ensemble mean emission of 596 Tg
CH
yr
−1
[572–614] for 2017. Global emissions for 2000–2009
(547 Tg
CH
yr
−1
) are consistent with Saunois et al. (2016)
and the range for global emissions; 524–560 Tg
CH
yr
−1
is in line
with the range in Saunois et al. (2016)
(535–569), although the ensemble of inverse systems contributing to this
budget is different than for Saunois et al. (2016).
Indeed, only six inverse systems of the nine examined here (Table S7)
contributed previously to the Saunois et al. (2016)
budget. The range reported gives the minimum and maximum values among
studies and does not reflect the individual full uncertainties. Also, most of
the top-down models use the same OH distribution from the TRANSCOM
experiment
(Patra et al., 2011), leading to a global budget that is quite constrained, probably explaining the rather low range (10 %) compared to bottom-up estimates (see below).
The estimates made via the bottom-up approaches
considered here are quite different from the top-down results, with global
emissions almost 30 % larger, at 737 Tg
CH
yr
−1
[594–881] for
2008–2017 (Table 3). Moreover, the range estimated using bottom-up
approaches does not overlap with that of the top-down estimates. The
bottom-up estimates are given by the sum of individual anthropogenic and
natural processes, without any constraint on the total. For the period
2000–2009, the discrepancy between bottom-up and top-down was 30 % of the
top-down estimates in Saunois et al. (2016)
(167 Tg
CH
yr
−1
); this has been reduced only slightly (now 156 Tg
CH
yr
−1
for the same
2000–2009 period). This reduction is due to
(1) a better agreement in the anthropogenic emissions (top-down and bottom-up
difference reducing from 19 to 2 Tg
CH
yr
−1
), (2) a reduction in the estimates of some natural sources other
than wetlands based on recent literature (by 7 Tg
CH
yr
−1
from
geological sources, by 8 Tg
CH
yr
−1
from wild animals, and by 3 Tg
CH
yr
−1
from allocation of wildfires to biomass & biofuel
burning; see Table 3), and (3) a reduction of 35 Tg
CH
yr
−1
in the
bottom-up estimates of wetland emissions by models when excluding lakes and
paddies as wetlands (see Sect. 5.1.2 below). These reductions (
−70
Tg
CH
yr
−1
) in the bottom-up budget are partially offset by revised
freshwater emissions with higher values (
37 Tg
CH
yr
−1
resulting from the integration of a recent study on lake, pond, and reservoir
emissions (DelSontro et al., 2018; see Sect. 3.2.2) and the integration of estuary emissions in this budget (
4 Tg
CH
yr
−1
). Overall, the uncertainty range of some natural
emissions has decreased in this study compared to Kirschke et al. (2013)
and Saunois et al. (2016),
for example for oceans, termites, wild animals, and geological sources.
However, the uncertainty in the global budget remains high because of the
large range reported for emissions from freshwater systems. Still, as noted
in Kirschke et al. (2013),
such large global emissions from the bottom-up approaches are not consistent
with top-down estimates that rely on OH burden constrained by
methyl chloroform atmospheric observations, and are very likely
overestimated. This overestimation likely results from errors related to
up-scaling local measurements and double-counting of some natural sources
(e.g. wetlands, other inland water systems; see Sect. 5.1.2).
Figure 5
Methane global emissions from the five broad categories (see Sect. 2.3) for the 2008–2017 decade for top-down inversion models (left light
coloured box plots, Tg
CH
yr
−1
) and for bottom-up models and
inventories (right dark coloured box plots). Median value and first and third
quartiles are presented in the boxes. The whiskers represent the minimum and
maximum values when suspected outliers are removed (see Sect. 2.2).
Suspected outliers are marked with stars when they exist. Bottom-up quartiles
are not available for bottom-up estimates, except for wetland emissions.
Mean values are represented with “
” symbols; these are the values
reported in Table 3.
Figure 6
Global methane budget for the 2008–2017 decade. Both bottom-up
(left) and top-down (right) estimates (Tg
CH
yr
−1
) are provided for each emission and
sink category, as well as for total emissions and
total sinks. Biomass and biofuel burning emissions are depicted here as both
natural and anthropogenic emissions while they are fully included in
anthropogenic emissions in the budget tables and text (Sect. 3.1.5).
5.1.2
Global methane emissions per source category
The global methane emissions from natural and anthropogenic sources (see
Sect. 2.3) for 2008–2017 are presented in Figs. 5 and 6 and Table 3.
Top-down estimates attribute about 60 % of total emissions to
anthropogenic activities (range of 55 %–70 %) and 40 % to natural
emissions. As natural emissions estimated from bottom-up approaches are much
larger, the anthropogenic versus natural emission ratio is nearly 1, not
consistent with ice core data. A current predominant role of anthropogenic
sources of methane emissions is consistent with and strongly supported
by
available ice core and atmospheric methane records. These data indicate that
atmospheric methane varied around 700 ppb during the last millennium before
increasing by a factor of 2.6 to
1800 ppb since
pre-industrial times. Accounting for the decrease in mean lifetime over the
industrial period, Prather et al. (2012)
estimated from these data a total source of
554±56
Tg
CH
in
2010 of which about 64 % (
352±45
Tg
CH
) was of anthropogenic
origin, consistent with the range in our top-down estimates.
For wetlands in 2008–2017, the top-down and bottom-up estimates of 181 Tg
CH
yr
−1
(range 159–200) and 149 Tg
CH
yr
−1
(range
102–182), respectively, are statistically consistent. Bottom-up mean wetland
emissions for the 2000–2009 period are smaller in this study than those of
Saunois et al. (2016).
Conversely, the current 2000–2009 mean top-down wetland estimates are larger
than those of Saunois et al. (2016)
(Table 3). The reduction in wetland emissions from bottom-up models is
related to an updated wetland extent dataset (WAD2M; see Sect. 3.2.1).
Top-down wetland emissions estimates are higher on average but the range is
reduced by 50 % compared to Saunois et al. (2016)
for 2000–2009. In the bottom-up estimates, the amplitude of the range of
emissions of 102–179 is similar to that in Saunois et al. (2016)
(151–222 for 2000–2009), and narrowed by a third compared to the previous
estimates from Melton et al. (2013) (141–264) and from
Kirschke et al. (2013)
(177–284). Here and in Saunois et al. (2016),
the land surface models were forced with the same wetland extent and climate
forcing (see Sect. 3.2.1) contrary to Melton et al. (2013) and Kirschke et al. (2013).
This suggests that differences in wetland extent explain about a third
(30 %–40 %) of the former range of the emission estimates of global natural
wetlands. The remaining range is due to differences in model structures and
parameters. Bottom-up and top-down estimates for wetland emissions differ
more in this study (
30 Tg yr
−1
for the mean) than in
Saunois et al. (2016)
17 Tg yr
−1
), due to reduced estimates from the
bottom-up models and increased estimates from the top-down models. Natural
emissions from freshwater systems are not included in the prior fluxes
entering the top-down approaches. However, emissions from these non-wetland
systems may be accounted for in the posterior estimates of the top-down
models, as these two sources are close and probably overlap at the rather
coarse resolution of the top-down models. In the top-down budget, natural
wetlands represent 30 % on average of the total methane emissions but only
22 % in the bottom-up budget (because of higher total emissions inferred).
Neither bottom-up nor top-down approaches included in this study point to
significant changes in wetland emissions between the two decades 2000–2009
and 2008–2017 at the global scale.
For other natural emissions, the discrepancy between top-down and bottom-up
budgets is the largest for the natural emission total, which is 371 Tg
CH
yr
−1
[245–488] for bottom-up and only 218 Tg
CH
yr
−1
[183–248] for top-down over the 2008–2017 decade. This discrepancy
comes from the estimates in “other natural” emissions (freshwater systems,
geological sources, termites, oceans, and permafrost). Indeed, for the
2008–2017 decade, top-down inversions infer non-wetland emissions of 37 Tg
CH
yr
−1
[21–50], whereas the sum of the individual bottom-up
emissions is 222 Tg
CH
yr
−1
[143–306]. Atmospheric inversions
infer about the same amount over the decade 2000–2009 as over 2008–2017,
which is almost half of the value reported in Saunois et al. (2016)
(68 [21–130] Tg
CH
yr
−1
). This reduction is due to either (1) a
more consistent way of considering other natural emissions in the various
inverse systems or (2) a difference in the ensemble of top-down inversions
reported here. It is worth noting that lacking gridded products to use in
their prior scenarios, most of the top-down models include only ocean and termite
emissions in their prior scenarios. Some of them now include geological
sources, but none include freshwater or permafrost emissions in their prior
fluxes and thus in their posterior estimates. Regarding the bottom-up
budget, the two main contributors to the larger bottom-up total are
freshwaters (
75 %) and geological emissions
15 %), both of which have large uncertainties and lack of
spatially explicit representation with gridded products available to date,
for freshwaters for
example. Because of the discrepancy, the category
other natural represents 7 % of total emissions in the top-down
budget but up to 25 % in the bottom-up budget.
Geological emissions are associated with relatively large uncertainties, and
marine seepage emissions are still widely debated
(Thornton et al., 2020). However,
summing up all bottom-up fossil-
CH
-related sources (including the
anthropogenic emissions) leads to a total of 173 Tg
CH
yr
−1
[131–219] in 2008–2017, which is about 30 % of the top-down global methane
emissions and 23 % of the bottom-up total global estimate. These results
agree with the value inferred from
14
C atmospheric isotopic analyses of
30 % contribution of fossil
CH
to global emissions
(Etiope et al.,
2008; Lassey et al., 2007b). This total fossil fuel emissions from bottom-up
approaches agree well with the
13
-based estimate of Schwietzke et al. (2016) at
192±32
Tg
CH
yr
−1
Uncertainties on bottom-up estimates of natural emissions lead to probably
overestimated total methane emissions resulting in a lower contribution
compared to Lassey et al. (2007b). All non-geological and
non-freshwater land source categories (wild animals, termites, permafrost)
have been estimated to be lower than in Kirschke et al. (2013)
and Saunois et al. (2016)
and contribute only 13 Tg
CH
yr
−1
[4–19] to global emissions.
From a top-down point of view, the sum of all the natural sources is more
robust than the partitioning between wetlands and other natural sources.
Better constraining the partitioning of methane emissions between wetlands
and freshwater systems, including emissions from thawing permafrost, may be
the key to reconcile the top-down and bottom-up budget on natural sources. Also,
including all known spatio-temporal distributions of natural emissions in
top-down prior fluxes would be a step forward to consistently compare
natural versus anthropogenic total emissions between top-down and bottom-up
approaches.
Total anthropogenic emissions for the period
2008–2017 were assessed to be statistically consistent between top-down (359 Tg
CH
yr
−1
, range 336–376) and bottom-up approaches (366 Tg
CH
yr
−1
, range 349–393). The partitioning of anthropogenic
emissions between agriculture and waste, fossil fuel extraction and use,
and biomass and biofuel burning also shows good consistency between
top-down and bottom-up approaches, though top-down approaches suggest less
fossil fuel and more agriculture and waste emissions than bottom-up
estimates (Table 3 and Figs. 5 and 6). For 2008–2017, agriculture and waste
contributed an estimated 217 Tg
CH
yr
−1
[207–240] for the
top-down budget and 206 Tg
CH
yr
−1
[191–223] for the bottom-up
budget. Fossil fuel emissions contributed 111 Tg
CH
yr
−1
[81–131]
for the top-down budget and 128 Tg
CH
yr
−1
[113–154] for the
bottom-up budget. Biomass and biofuel burning contributed 30 Tg
CH
yr
−1
[22–36] for the top-down budget and 30 Tg
CH
yr
−1
[26–40] for the bottom-up budget. Biofuel methane
emissions rely on very few estimates currently
(Wuebbles and Hayhoe, 2002). Although biofuel is
a small source globally (
12 Tg
CH
yr
−1
), more
estimates are needed to allow a proper uncertainty assessment. Overall
for
top-down inversions the global fraction of total emissions for the different
source categories is 38 % for agriculture and waste, 19 % for fossil
fuels, and 5 % for biomass and biofuel burning. With the exception of
biofuel emissions, the uncertainty associated with global anthropogenic
emissions appears to be smaller than that of natural sources but with an
asymmetric uncertainty distribution (mean significantly different than
median). The relative agreement between top-down and bottom-up approaches
may indicate the limited capability of the inversion to separate the
emissions and should therefore be treated with caution. Indeed, in poorly
observed regions, top-down inversions rely on the prior estimates and bring
little or no additional information to constrain (often) spatially
overlapping emissions (e.g. in India, China). Also, as many top-down systems
solve for the total fluxes at the surface or for some categories that may
differ from the GCP categories, their posterior partitioning relies on the
prior ratio between categories that are prescribed using bottom-up
inventories.
5.1.3
Global budget of total methane sinks
For top-down estimates, the
CH
chemical removal from the atmosphere is
estimated at 518 Tg
CH
yr
−1
over the period 2008–2017, with an
uncertainty of about
5 % (range 474–532 Tg
CH
yr
−1
).
All the inverse models account for
CH
oxidation by OH and O(
D),
and some include stratospheric chlorine oxidation (Table S6). In addition,
most of the top-down models use OH distributions from the TRANSCOM experiment
(Patra et al., 2011), probably explaining the rather low range of estimates
compared to bottom-up estimates (see below). Differences between transport
models affect the chemical removal of
CH
, leading to different
chemical loss rates, even with the same OH distribution. However,
uncertainties in the OH distribution and magnitude
(Zhao et al., 2019) are not considered in our study, while it could contribute to a
significant change in the chemical sink and then in the derived posterior
emissions through the inverse process
(Zhao et al., 2020). The
chemical sink represents more than 90 % of the total sink, the rest being
attributable to soil uptake (38 [27–45] Tg
CH
yr
−1
). Half of the
top-down models use the climatological soil uptake magnitude (37–38 Tg
CH
yr
−1
) and distribution from Ridgwell et al. (1999), while half of the models use an estimate from
the biogeochemical model VISIT (Ito and Inatomi, 2012), which
calculates varying uptake between 31 and 38 Tg
CH
yr
−1
over the
2000–2017 period. These sink estimates used as the prior estimate in the inversions are
generally higher than the mean estimate of the soil sink calculated by
bottom-up models (30 Tg
CH
yr
−1
, Sect. 3.3.4).
For the bottom-up estimates, the total chemical loss for the 2000s reported here is
595 Tg
CH
yr
−1
with an uncertainty of 22 % (
130 Tg
CH
yr
−1
). Differences in chemical schemes (especially in
the stratosphere) and in the volatile organic compound treatment probably
explain most of the discrepancies among models
(Zhao et al.,
2019).
5.2
Latitudinal methane budget
5.2.1
Latitudinal budget of total methane emissions
The latitudinal breakdown of emissions inferred from atmospheric inversions
reveals a dominance of tropical emissions at 368 Tg
CH
yr
−1
[337–399], representing 64 % of the global total (Table 5). A total of 32 % of
the emissions are from the mid-latitudes (186 Tg
CH
yr
−1
[166–204]) and 4 % are from high latitudes (above 60
N). The ranges
around the mean latitudinal emissions are larger than for the global methane
sources. While the top-down uncertainty is about
5 % at the global
scale, it increases to
10 % for the tropics and the northern
mid-latitudes to more than
25 % in the northern high latitudes
(for 2008–2017, Table 5). Both top-down and bottom-up approaches
consistently show that methane decadal emissions have increased by about 20 Tg
CH
yr
−1
in the tropics and by 7–18 Tg
CH
yr
−1
in
the northern mid-latitudes between 2000–2009 and 2008–2017, but not in the
northern high latitudes.
Table 5
Global and latitudinal total methane emissions (Tg
CH
yr
−1
), as decadal means (2000–2009 and 2008–2017) and for the year 2017,
for this work using bottom-up and top-down approaches. Global emissions for
2000–2009 are also compared with Saunois et al. (2016) and Kirschke et al. (2013) for top-down and bottom-up approaches. Latitudinal total emissions
for 2000–2009 are compared with Saunois et al. (2016) for top-down studies
only. Uncertainties are reported as the [min–max] range. Differences of 1 Tg
CH
yr
−1
in the totals can occur due to rounding errors.
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Over 2010–2017, at the global scale, satellite-based inversions infer almost
identical emissions to ground-based inversions (difference of 3 [0–7] Tg
CH
yr
−1
), when consistently comparing surface versus satellite-based
inversions for each system. This difference is much lower than the range
derived between the different systems (range of 20 Tg
CH
yr
−1
using surface- or satellite-based inversions). This result reflects that
differences in atmospheric transport among the systems probably have more
impact than the types of observations assimilated on the estimated global
emissions. In Saunois et al. (2016),
satellite-based inversions reported 12 Tg higher global methane emissions
compared to surface-based inversions. Differences in the ensemble, the use
of only GOSAT data and the treatment of satellite data within each system
compared to Saunois et al. (2016)
explain the contrasting results.
As expected, the regional distributions of inferred emissions differ
depending on the nature of the observations used (satellite or surface). The
largest differences (satellite-based minus surface-based inversions) are
observed over the tropical region, between
−13
and
26 Tg
CH
yr
−1
(90
S to 30
N), and the northern
mid-latitudes (between
−20
and
15 Tg
CH
yr
−1
). Satellite data
provide stronger constraints on fluxes in tropical regions than surface
data, due to a much larger spatial coverage. It is therefore not surprising
that differences between these two types of observations are found in the
tropical band, and consequently in the northern mid-latitudes to balance
total emissions, thus affecting the north–south gradient of emissions.
However, the regional patterns of these differences are not consistent
through the different inverse systems. Indeed, some systems found higher
emissions in the tropics when using GOSAT instead of
surface observations,
while others found the opposite. This difference between the systems may
depend on whether or not a bias correction is applied to the satellite data
based on surface observations, and also on the modelled horizontal and
vertical model transports, in the troposphere and in the stratosphere.
Figure 7
Methane latitudinal emissions from the five broad categories (see
Sect. 2.3) for the 2008–2017 decade for top-down inversions models (left
light-coloured box plots, Tg
CH
yr
−1
) and for bottom-up models
and inventories (right dark-coloured box plots). Median value and first and
third quartiles are presented in the boxes. The whiskers represent the
minimum and maximum values when suspected outliers are removed (see Sect. 2.2). Suspected outliers are marked with stars as shown. Bottom-up quartiles
are not available for bottom-up estimates, except wetland emissions. Mean
values are represented with “
” symbols; these are the values reported
in Table 6.
Table 6
Latitudinal methane emissions in teragrams of
CH
per year for the last
decade 2008–2017, based on top-down and bottom-up approaches. Uncertainties
are reported as the [min–max] range of reported studies. Differences of 1 Tg
CH
yr
−1
in the totals can occur due to rounding errors. Bottom-up anthropogenic
estimates are based only on the gridded products from EDGARv4.3.2, GAINS, and CEDS.
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5.2.2
Latitudinal methane emissions per source category
The analysis of the latitudinal methane budget per source category (Fig. 7)
can be performed for both bottom-up and top-down approaches but with
limitations. On the bottom-up side, some natural emissions are not (yet)
available at regional scale (mainly inland waters). Therefore, for
freshwater emissions, we applied the latitudinal distribution of Bastviken
et al. (2011) to the global reported value. Further
details are provided in the Supplement to explain how the different
bottom-up sources were handled. On the top-down side, as already noted, the
partitioning of emissions per source category has to be considered with
caution. Indeed, using only atmospheric methane observations to constrain
methane emissions makes this partitioning largely dependent on prior
emissions. However, differences in spatial patterns and seasonality of
emissions can be utilized to constrain emissions from different categories
by atmospheric methane observations (for those inversions solving for
different sources categories, see Sect. 2.3).
Agriculture and waste are the largest sources of methane emissions in the
tropics (130 [121–137] Tg
CH
yr
−1
for the bottom-up budget and
139 [127–157] Tg
CH
yr
−1
for the top-down budget, about 38 % of
total methane emissions in this region). However, wetland emissions are
nearly as large with 116 [71–146] Tg
CH
yr
−1
for the bottom-up
budget and 135 [116–155] Tg
CH
yr
−1
for the top-down budget.
One top-down model suggests lower emissions from agriculture and waste
compared to the ensemble but suggests higher emissions from fossil fuel:
this recalls the necessary caution when discussing sectorial partitioning
when using top-down inversions. Anthropogenic emissions dominate in the
northern mid-latitudes, with the highest contribution from agriculture and
waste emissions (42 % of total emissions), closely followed by fossil fuel
emissions (31 % of total emissions). Boreal regions are largely dominated
by wetland emissions (60 % of total emissions).
The uncertainty for wetland emissions is larger in the bottom-up models than
in the top-down models, while uncertainty in anthropogenic emissions is
larger in the top-down models than in the inventories. The large uncertainty
in tropical wetland emissions (65 %) results from large regional
differences between the bottom-up land surface models. Although they are
using the same wetland extent, their responses in terms of flux density show
different sensitivities to temperature, water vapour pressure,
precipitation, and radiation.
More regional discussions were developed in Saunois et al. (2016)
and have been updated in Stavert et al. (2020).
Future developments, missing elements, and remaining uncertainties
In this budget, uncertainties on sources and sinks estimated by bottom-up or
top-down approaches have been highlighted as well as discrepancies between
the two budgets. Limitations of the different approaches have also been
highlighted. Four shortcomings of the methane budget were already identified
in Kirschke et al. (2013) and Saunois et al. (2016).
Although progress has been made, they are still relevant, and actions are
needed. However, these actions fall into different timescales and parties.
In the following, we revisit the four shortcomings, or axis of research, of
the current methane budget: how each weakness has been corrected since
Saunois et al. (2016), followed by a list of recommendations, from higher to lower priority,
associated with the involved parties.
Towards reducing the high uncertainty in the amount of methane emitted by wetland and inland water systems and reduced double-counting issue.
The remaining large uncertainties strongly suggest the need to develop more
studies integrating the different systems (wetlands, ponds, lakes,
reservoirs, streams, rivers, estuaries, and marine systems), to avoid double-counting issues, to associate proper emissions with each category, but
also to
account for lateral fluxes. Since Saunois et al. (2016),
several workshops (e.g. Turner et al., 2019) and publications
(e.g. Knox et al., 2019; Thornton et al., 2016a) contributed to implement previous
recommendations and strategies to reduce uncertainties of methane emissions
due to wetlands and other freshwater systems. One achievement is the reduced
estimate (by
20 %, i.e. 35 Tg
CH
yr
−1
) of the
global wetland emissions, due to a refined wetland extent analysis and
modifications of land surface model calibration.
Methodology changes that could be integrated into the next methane budget
releases include
calibrating land surface models independently from top-down estimates,
evaluating land surface models against in situ observations such as
FLUXNET-
CH
(Knox et al., 2019), and
using different wetland extent products to infer wetland emissions (e.g. WAD2M, GIEMS-2; Prigent et al., 2020).
Next steps, in the short term, for modelling, can be addressed by the land
biogeochemistry community.
Finalize a global high-resolution (typically tens of metres)
classification of saturated soils and inundated surfaces based on satellite
data (visible and microwave), surface inventories, and expert knowledge.
This improved area distribution will prevent double-counting between
wetlands and other freshwater systems, when used by land surface models.
Finalize ongoing efforts to develop process-based modelling approaches to
estimate freshwater methane emissions, including lateral fluxes, and
avoiding upscaling issues, as recently done by Maavara et al. (2019) for
Use the collected flux measurements within the FLUXNET-
CH
activity
(Knox et al., 2019) to provide global flux maps based on machine learning approaches (Peltola
et al., 2019).
Over the long run, developing measurement systems will help to improve
estimates of wetland and inland water sources, and further reduce
uncertainties.
More systematic measurements from sites reflecting the diverse lake
morphologies will allow us to better understand the short-term biological
control on ebullition variability, which remains poorly known
(Wik et al., 2014, 2016a).
Extending monitoring of methane fluxes year round from the different natural
sources (wetlands, freshwaters) complemented with environmental meta-data
(e.g., soil temperature and moisture, vegetation types, water temperature,
acidity, nutrient concentrations, NPP, soil carbon density) will allow us to
enrich the FLUXNET-
CH
observation dataset and to better constrain
methane fluxes and their isotopic signatures in land surface models
(Glagolev et al., 2011; Turetsky et al.,
2014; Ganesan et al., 2019).
2.
Towards a better assessment of uncertainties for global methane sinks in top-down and bottom-up budgets.
The inverse systems used here have the same caveats as described in Saunois
et al. (2016)
(same OH field, same kind of proxy method to optimize it), leading to quite
constrained atmospheric sink and therefore total global methane sources.
Although we have used a state-of-the-art ensemble of chemistry-transport models (CTMs) and climate–chemistry models (CCMs) simulations from the CCMI
(Chemistry-Climate Model Initiative, Morgenstern et al., 2017), the uncertainty of derived
CH
chemical loss from the chemistry–climate models remains at the same
(large) level compared to the previous intercomparison project ACCMIP
(Lamarque et al.,
2013). Nicely et al. (2017)
found that the main cause of the large differences in the CTM representation
of
CH
lifetime is variations in the chemical mechanisms implemented in the models. Using the ensemble of CTMs and CCMs from the CCMI experiment, Zhao et al. (2019)
quantified the range of
CH
loss induced by the ensemble of OH fields
to be equivalent up to about half of the discrepancies between
CH
observations and simulations as forced by the current anthropogenic
inventories. These results emphasize the need to first assess, and then
improve, atmospheric transport and chemistry models, especially vertically,
and to integrate robust representation of OH fields in atmospheric models.
Methodology changes that could be integrated into the next methane budget
include
integrating sensitivity tests on the prior fluxes (use of updated fluxes for natural sources, soil uptake) and
integrating sensitivity tests on chemical sinks (different OH fields,
including inter-annual variability).
Next steps, in the short term, could include developments by the atmospheric
modelling community.
Assess the impact of using updated and varying soil uptake estimates,
especially considering a warmer climate (Ni and Groffman, 2018). Indeed, for top-down models
resolving for the net flux of
CH
at the surface integrating a larger
estimate of soil uptake would allow larger emissions and then reduce the
uncertainty with the bottom-up estimates of total
CH
sources.
Further study the reactivity of the air parcels in the chemistry–climate
models and define new diagnostics to assess modelled
CH
lifetimes.
Develop robust representation of 3D OH fields to be used in the inverse
models: based on chemistry–climate models and using correction from
measurements, on multispecies assimilating systems
(e.g.
Gaubert et al., 2017; Miyazaki et al., 2015), or on a simple parametrization
applied at grid scales.
Integrate the aforementioned different potential OH chemical fields,
also including inter-annual variability, to assess the impact on the methane
budget following Zhao et al. (2020).
Over the long run, other parameters should be (better) integrated into
top-down approaches, among them
the magnitude of the
CH
loss through oxidation by tropospheric
chlorine, a process debated in the recent literature. More modelling
(Thanwerdas et al., 2019) and
instrumental studies should be devoted to reducing the uncertainty of this
potential additional sink before integrating it in top-down models.
3.
Towards a better partitioning of methane sources and sinks by region and process using top-down models.
In this work, we report inversions assimilating satellite data from GOSAT,
which bring more constraints than provided by surface stations alone,
especially over tropical continents. However, we found that satellite- and
surface-based inversions and the different inversions systems do not
consistently infer the same regional flux distribution.
Methodology changes that could be integrated into the next methane budget
releases include the following.
Integrate GOSAT and GOSAT-2 (launched in October 2018, with expected
improved precision and accuracy, JAXA, 2019) for the satellite
inversion.
Investigate the reasons for the regional differences derived by the
inverse systems based on the model evaluation and a more detailed
questionnaire for the modellers on the treatment of satellite data (bias
correction) and stratospheric profiles.
Next steps, in the short term, could integrate developments to be made by
the top-down community.
Evaluate the benefits of using new satellite missions with high spatial
resolution and “imaging capabilities” (Crisp et al., 2018) at
the global scale, such as the TROPOMI instrument on Sentinel 5P, launched in
October 2017 (Hu et al., 2018).
Integrate the newly available updated gridded products for the different
natural sources of
CH
in their prior fluxes to reach a full spatial
description of sources and sinks and to be able to better compare the
top-down budget with the bottom-up budget.
Release more regular updates and intercomparison of emission inventories
in order to improve prior scenarios of inverse studies and reduce the need
for extending them beyond their available coverage.
Develop a 4D variational inversion system using isotopic and/or co-emitted
species in the top-down budget. Indeed methane isotopes can provide
additional constraints to partition the different
CH
sources and
sinks, if isotopic signatures can be better known spatially and temporally
(Ganesan et al., 2018). Radiocarbon
can help for fossil and non-fossil emissions
(Lassey
et al., 2007b, a; Petrenko et al., 2017),
13
CH
and CH
for biogenic–pyrogenic–thermogenic emissions and OH loss
(Röckmann et al., 2011), and emerging
clumped isotope measurements for biogenic–thermogenic emissions
(Stolper et al., 2014) and OH
loss (Haghnegahdar et al., 2017). Also, carbon
monoxide (e.g. Fortems-Cheiney et al., 2011) can provide useful constraints
for biomass burning emissions and ethane for fugitive emissions
(e.g. Simpson et al.,
2012; Turner et al., 2019).
Improve the availability of in situ data for the scientific community, especially ones covering poorly documented regions such as China (Fang et al., 2015), India
(Lin et al., 2015; Tiwari and Kumar, 2012) and Siberia
(Sasakawa et al., 2010; Winderlich et al., 2010), which have not been included so far in international databases.
Over the long run, integrating more measurements and regional studies will
help to improve the top-down systems, and further reduce the uncertainties.
Integrate global data from future satellite instruments with intrinsic low bias, such as active lidar techniques with MERLIN (Ehret et al., 2017), that are promising to overcome issues of systematic errors (Bousquet et al., 2018) and should
provide measurements over the Arctic, contrary to the existing and planned
passive missions.
Extend the
CH
surface networks to poorly observed regions (e.g. the tropics, China, India, high latitudes) and to the vertical dimension: regular aircraft campaigns
(e.g. Paris et al., 2010; Sweeney et al., 2015), AirCore campaigns (e.g.
Andersen et al., 2018; Membrive et al., 2017), and TCCON observations
(e.g. Wunch et al., 2011, 2019). These observations are still critical to complement
satellite data that do not observe well in cloudy regions and at high
latitudes and also to evaluate and eventually correct satellite biases
(Buchwitz et al., 2017).
Extend and develop continuous isotopic measurements of methane using laser-based instruments to help partition methane sources and to be integrated in 4D variational isotopic inversions.
Develop regional components of the
CH
budget to improve global totals by feeding them with regional top-down and bottom-up approaches; for example, regional inversions using regional measurements and high-resolution models, such as the INGOS project
(Bergamaschi et al., 2018b; INGOS, 2018) or the VERIFY project (
, last access: 29 June 2020) with the
European ICOS network (ICOS-RI, 2019,
, last access: 29 June 2020). The RECCAP-2 project
should also provide a scientific framework to further refine greenhouse gas budgets,
including methane, at regional scales
, last access: 29 June 2020).
4.
Towards reducing uncertainties in the modelling of atmospheric transport in the models used in the top-down budget.
The TRANSCOM experiment synthesized in Patra et al. (2011)
showed a large sensitivity of the representation of atmospheric transport to
methane concentrations in the atmosphere. In particular, the modelled
CH
budget appeared to depend strongly on the troposphere–stratosphere
exchange rate and thus on the model vertical grid structure and circulation
in the lower stratosphere. Also, regional changes in the methane budget
depend on the characteristics of the atmospheric transport models used in
the inversion
(Bruhwiler et al., 2017; Locatelli et al., 2015). This axis of research is demanding important development from the atmospheric modelling community. Waiting for future improvements (finer horizontal and vertical resolutions, more
accurate physical parameterization, increase in computing
resources, etc.) and assessing atmospheric transport error and the
impact on the top-down budget remain crucial.
Methodology changes that could be integrated into the next methane budget
releases include
evaluation of the inversions provided against independent measurements such as regular aircraft campaigns
(e.g. Paris et al., 2010; Sweeney et al., 2015), AirCore campaigns (e.g.
Andersen et al., 2018; Membrive et al., 2017), and TCCON observations
(e.g. Wunch et
al., 2011, 2019) and use of this evaluation to weight the different models
used in the methane budget.
Next steps, in the short term, could include some development to be
addressed by the top-down community to reduce atmospheric transport errors:
developing further methodologies to extract stratospheric partial column
abundances from observations such as TCCON data
(Saad et al., 2014; Wang et
al., 2014), AirCore, or even ACE-FTS or MIPAS satellite data and using them
to replace erroneous simulated stratospheric profiles.
In the long run, developments within atmospheric transport models such as
the implementation of hybrid vertical coordinates
(Patra et al., 2018) or of a
hexagonal-icosaedric grid with finer resolution
(Dubos
et al., 2015; Niwa et al., 2017a) and improvements in the simulated
boundary layer dynamics are promising to reduce atmospheric transport
errors.
Data availability
The data presented here are made available in the belief that their
dissemination will lead to greater understanding and new scientific insights
into the methane budget and changes to it and help to reduce its
uncertainties. The free availability of the data does not constitute
permission for publication of the data. For research projects, if the data
used are essential to the work to be published, or if the conclusion or
results largely depend on the data, co-authorship should be considered. Full
contact details and information on how to cite the data are given in the
accompanying database.
The accompanying database includes one Excel file organized in the following
spreadsheets and two NetCDF files defining the regions used to extend the
anthropogenic inventories.
The file Global_Methane_Budget_2000–2017_v2.0.xlsx includes (1) a summary, (2) the methane
observed mixing ratio and growth rate from the four global networks (NOAA,
AGAGE, CSIRO and UCI), (3) the evolution of global anthropogenic methane
emissions (including biomass burning emissions) used to produce Fig. 2, (4) the global and latitudinal budgets over 2000–2009 based on bottom-up
approaches, (5) the global and latitudinal budgets over 2000–2009 based on
top-down approaches, (6) the global and latitudinal budgets over 2008–2017
based on bottom-up approaches, (7) the global and latitudinal budgets over
2008–2017 based on top-down approaches, (8) the global and latitudinal
budgets for the year 2017 based on bottom-up approaches, (9) the global and
latitudinal budgets for the year 2017 based on top-down approaches, and (10) the
list of contributors to contact for further information on specific data.
This database is available from ICOS (
Saunois et al., 2020) and the Global Carbon Project
, last access: 29 June 2020).
Conclusions
We have built a global methane budget by using and synthesizing a large
ensemble of new and published methods and results using a consistent and transparent
approach, including atmospheric observations and inversions (top-down
models), process-based models for land surface emissions and atmospheric
chemistry, and inventories of anthropogenic emissions (bottom-up models and
inventories). For the 2008–2017 decade, global
CH
emissions are 576 Tg
CH
yr
−1
(range of 550–594 Tg
CH
yr
−1
), as
estimated
by top-down inversions. About 60 % of global emissions are anthropogenic
(range of 50 %–70 %). Bottom-up models and inventories suggest much larger
global emissions (737 Tg
CH
yr
−1
[594–881]) mostly because of
larger and more uncertain natural emissions from inland water systems,
natural wetlands, and geological leaks and some likely unresolved double-counting of these sources. It is also likely that some of the individual
bottom-up emission estimates are too high, leading to larger global
emissions from the bottom-up perspective than the atmospheric constraints
suggest.
The latitudinal breakdown inferred from top-down approaches reveals a
dominant role of tropical emissions (
64 %) compared to middle
32 %) and high (
4 %) northern latitude
(above 60
N) emissions.
Our results, including an extended set of atmospheric inversions, are
compared with the previous budget syntheses of Kirschke et al. (2013)
and Saunois et al. (2016)
and show overall good consistency when comparing the same decade (2000–2009)
at the global and latitudinal scales, although estimation methods and
reported studies have evolved between the three budgets. While a comparison
of top-down emissions estimates determined with and without satellite data
agrees well globally, they differ significantly at the latitudinal scale.
Most worryingly, these differences were not even consistent in sign, with
some models showing notable increases in a given latitudinal flux and others
decreases. This suggests that while the inclusion of satellite data may, in
the future, significantly increase our ability to attribute fluxes
regionally, this is not currently the case due to their existing inherent
biases along with the inconsistent application of methods to account for
these biases and also differences in model transport, especially in the
stratosphere (see recommendations in Sect. 6).
Among the different uncertainties raised in Kirschke et al. (2013),
Saunois et al. (2016)
estimated that 30 %–40 % of the large range associated with modelled wetland
emissions in Kirschke et al. (2013)
was due to the estimation of wetland extent. Here, wetland emissions are 35 Tg
CH
yr
−1
smaller than previous estimates due to a refinement of
wetland extent. The magnitude and uncertainty of all other natural sources
have been revised and updated, leading to smaller emission estimates for
oceans, geological sources, and wild animals but higher emission estimates
associated with a larger range for non-wetland freshwater systems. This result
places a number one priority on reducing uncertainties in emissions from
inland water systems by better quantifying the emission factors of each
contributing subsystem (streams, rivers, lakes, ponds) and reducing both
uncertain up-scaling and likely double-counting with wetland emissions. As a
second priority, the uncertainty on the chemical loss of methane needs to be
better assessed in both the top-down and the bottom-up budgets. Our work
also suggests the need for more interactions among groups developing
emission inventories in order to clarify the definition of the sectoral
breakdown in inventories. Such an approach would allow easier comparisons at
the subcategory scale.
Building on the improvement of the points detailed in Sect. 6, our aim is to continually
update this budget synthesis as a living review paper regularly
every 2–3 years). Each update will produce a more
recent decadal
CH
budget, highlight changes in emissions and trends,
and incorporate newly available data and model improvements.
In addition to the decadal
CH
budget presented in this paper, and
following former studies
(e.g. Bousquet
et al., 2006), trends and year-to-year changes in the methane cycle
continue to be thoroughly discussed in the recent literature
(e.g.
Nisbet et al., 2019; Turner et al., 2019). After almost a decade of
stagnation in the late 1990s and early 2000s (Dlugokencky
et al., 2011; Nisbet et al., 2016), a sustained atmospheric growth rate of
more than
5 ppb yr
−1
has been observed since 2007, with a further
acceleration after 2014
(Nisbet
et al., 2019) and several years with a two-digit atmospheric growth as in the
1980s. To date, no consensus has yet been reached in explaining the
CH
trend since 2007. A likely explanatory scenario, already introduced in
Saunois et al. (2017)
and further investigated by some other studies since then, includes, by
decreasing order of certainty, a positive contribution from microbial and
fossil sources
(e.g.
Nisbet et al., 2019; Schwietzke et al., 2016), a negative contribution from
biomass burning emissions before 2014 (Giglio et
al., 2013; Worden et al., 2017), a downward revision of Chinese emissions, a
negligible role of Arctic emission changes
(e.g.
Nisbet et al., 2019; Saunois et al., 2017), and a tropical dominance of the
increasing emissions (e.g.
Saunois et al., 2017). Changes in atmospheric OH concentrations, the largest
methane sink, may have contributed to the recent increase in methane
concentrations
(e.g.
Dalsøren et al., 2016; Holmes et al., 2013; McNorton et al., 2016, 2018;
Morgenstern et al., 2018; Rigby et al., 2017; Turner et al., 2017), but
considerable uncertainty in OH inter-annual variability and trends needs to be
further investigated. The challenging and sustained increase in atmospheric
CH
during the past decade still needs additional research to be fully understood
(Nisbet et al., 2019; Turner et al., 2019). The GCP will continue to take its part
in analysing and synthesizing recent changes in the global to regional
methane cycle based on the ensemble of top-down and bottom-up studies
gathered for the budget analysis presented here.
Appendix A
Table A1
Funding supporting the production of the various components of the
global methane budget in addition to the authors' supporting institutions
(see also Acknowledgements).
Download XLSX
Appendix:
Note on former version
A former version of this article was published on 12 December 2016 and is available at
Supplement
The supplement related to this article is available online at:
Author contributions
MS, AS, and BP gathered the bottom-up and top-down datasets and performed the
post-processing and analysis.
MS, AS, BP, PB, PeC, and RJ coordinated the global budget. MS, AS, BP, PB,
PeC, RJ, SH, PP, and PCi contributed to the update of the full text and all
coauthors appended comments. MS, ED, and GP produced the figures. VA, NG, AI,
FJ, TK, LL, KMcD, PM, JMe, JMu, CP, SP, WR, HS, HT, WZ, ZZ, QinZ, QiuZ,
and QiaZ performed surface land model simulations to compute wetland
emissions. DB, MC, PC, SC, KC, GE, GH, KMJ, GL, SN, CP, PRa, Pre, BT, NV, and TW
provided datasets useful for natural emission estimates and/or contributed
to text on bottom-up natural emissions. LHI, GJM, FT, GvW, and KMC provided
anthropogenic datasets and contributed to the text for this section. PP, BP,
NC, MI, SM, JMcN, YN, AS, AT, YY, and BZ performed atmospheric inversions to
compute top-down methane emission estimates. DRB, GB, CCr, CF, PK, RL, TM,
IM, SO'D, RJP, RP, MR, IJS, PS, YT, RFW, DWo, DWu, and YYo are PIs of atmospheric
observations used in top-down inversions and/or contributed the text
describing atmospheric methane observations. YZ, MvW, AV, VN, and MIH contributed
to the chemical sink section by providing datasets, processing data, and/or
contributed to the text. FMF and CCu provided data for the soil sink and
contributed to the text of this section.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO.
Acknowledgements
This paper is the result of a collaborative international effort under the
umbrella of the Global Carbon Project, a project of Future Earth and a
research partner of the World Climate Research Programme. We acknowledge all
the people and institutions who provided the data used in the global methane
budget as well as the institutions funding parts of this effort (see Table A1). We acknowledge the modelling groups for making their simulations
available for this analysis, the joint WCRP SPARC/IGAC Chemistry-Climate
Model Initiative (CCMI) for organizing and coordinating the model data
analysis activity, and the British Atmospheric Data Centre (BADC) for
collecting and archiving the CCMI model output.We acknowledge Adrian
Gustafson for his contribution to prepare the simulations of LPJ-GUESS. Paul A. Miller, Adrian Gustafson, and Wenxin Zhang acknowledge this work as a
contribution to the Strategic Research Area MERGE. FAOSTAT data collection,
analysis, and dissemination are funded through FAO regular budget funds. The
contribution of relevant experts in member countries is gratefully
acknowledged. We acknowledge Juha Hatakka (FMI) for making methane
measurements at the Pallas station and sharing the data with the community.
Financial support
Please see a full list of funders in the Appendix (Table A1).
Review statement
This paper was edited by David Carlson and reviewed by Michael Prather and one anonymous referee.
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Articles
Abstract
Introduction
Methodology
Methane sources and sinks: bottom-up estimates
Atmospheric observations and top-down inversions
Methane budget: top-down and bottom-up comparison
Future developments, missing elements, and remaining uncertainties
Data availability
Conclusions
Appendix A
Appendix:
Note on former version
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
Supplement
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Short summary
Understanding and quantifying the global methane (CH
) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Understanding and quantifying the global methane (CH
) budget is important for assessing...
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Sections
Abstract
Introduction
Methodology
Methane sources and sinks: bottom-up estimates
Atmospheric observations and top-down inversions
Methane budget: top-down and bottom-up comparison
Future developments, missing elements, and remaining uncertainties
Data availability
Conclusions
Appendix A
Appendix:
Note on former version
Author contributions
Competing interests
Disclaimer
Acknowledgements
Financial support
Review statement
References
Supplement