The global methane (CH
For the 2003–2012 decade, global methane emissions are estimated by top-down
inversions at 558 Tg CH
The most important source of uncertainty on the methane budget is
attributable to emissions from wetland and other inland waters. We show that
the wetland extent could contribute 30–40 % on the estimated range for
wetland emissions. Other priorities for improving the methane budget include the following:
(i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux
measurements) to constrain bottom-up land surface models, and at regional scale
(surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH,
and (iv) improvements of the transport models integrated in top-down inversions. The data
presented here can be downloaded from the Carbon Dioxide Information Analysis
Center (
The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copyright work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce or limit each other. © Crown copyright 2016
The surface dry air mole fraction of atmospheric methane (CH
Globally averaged atmospheric CH
Changes in the magnitude and timing (annual to interannual) of individual methane sources and sinks over the past decades are uncertain (Kirschke et al., 2013) with relative uncertainties (hereafter reported as min–max ranges) of 20–30 % for inventories of anthropogenic emissions in each sector (agriculture, waste, fossil fuels) and for biomass burning, 50 % for natural wetland emissions and reaching 100 % or more for other natural sources (e.g. inland waters, geological). The uncertainty in the global methane chemical loss by OH, the predominant sink, is estimated between 10 % (Prather et al., 2012) and 20 % (Kirschke et al., 2013), implying a similar uncertainty in global methane emissions as other sinks are much smaller and the atmospheric growth rate is well defined (Dlugokencky et al., 2009). Globally, the contribution of natural emissions to the total emissions is reasonably well quantified by combining lifetime estimates with reconstructed preindustrial atmospheric methane concentrations from ice cores (e.g. Ehhalt et al., 2001). Uncertainties in emissions reach 40–60 % at regional scale (e.g. for South America, Africa, China and India). Beyond the intrinsic value of characterizing the biogeochemical cycle of methane, understanding the evolution of the methane budget has strong implications for future climate emission scenarios. Worryingly, the current emission trajectory is tracking the warmest of all IPCC scenarios, the RCP8.5, and is clearly inconsistent with lower temperature scenarios, which show substantial to large reductions of methane emissions (Collins et al., 2013).
Reducing uncertainties in individual methane sources, and thus in the
overall methane budget, is not an easy task for, at least, four reasons.
First, methane is emitted by a variety of processes that need to be
understood and quantified separately, both natural or anthropogenic, point
or diffuse sources, and associated with three main emission processes
(biogenic, thermogenic and pyrogenic). Among them, several important
anthropogenic CH
The regional constraints brought by atmospheric sampling on atmospheric inversions are significant for northern midlatitudes thanks to a number of high-precision and high-accuracy surface stations (Dlugokencky et al., 2011). The atmospheric observation density has improved in the tropics with satellite-based column-averaged methane mixing ratios (Buchwitz et al., 2005b; Frankenberg et al., 2005; Butz et al., 2011). However, the optimal usage of satellite data remains limited by systematic errors in satellite retrievals (Bergamaschi et al., 2009; Locatelli et al., 2015). The development of low-bias observations system from space, such as active lidar technics, is promising to overcome these issues (Kiemle et al., 2014). The partition of regional emissions by processes remains very uncertain today, waiting for the development or consolidation of measurements of more specific tracers, such as methane isotopes or ethane, dedicated to constrain the different methane sources or groups of sources (e.g. Simpson et al., 2012; Schaefer et al., 2016; Hausmann et al., 2016).
The Global Carbon Project (GCP) aims at developing a complete picture of the
carbon cycle by establishing a common, consistent scientific knowledge to
support policy debate and actions to mitigate the rate of increase of
greenhouse gases in the atmosphere (
The work of Kirschke et al. (2013) was the first GCP-like CH
Five sections follow this introduction. Section 2 presents the methodology to treat and analyse the data streams. 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 the atmospheric observations and the top-down inversions gathered for this paper. Section 5, based on Sects. 3 and 4, provides an analysis of the global methane budget (Sect. 5.1) and of the regional methane budget (Sect. 5.2). Finally Sect. 6 discusses future developments, missing components and the largest remaining uncertainties after this update on the global methane budget.
Unless specified, the methane budget is presented in teragrammes of CH
The CH
Common data analysis procedures have been applied to the different bottom-up
models, inventories and atmospheric inversions whenever gridded products
exist. The monthly or yearly fluxes (emissions and sinks) provided by
different groups were processed similarly. They were re-gridded on a common
grid (1
Most budgets are presented as boxplots, which have been created using
routines in IDL language, provided with the standard version of the IDL
software. The values presented in the following are calculated using the
classical conventions of boxplots including quartiles (25 %, median,
75 %), outliers, and minimum and maximum values (without the outliers).
Outliers are determined as values below the first quartile minus 3 times
the interquartile range or values above third quartile plus 3 times the
interquartile range. Identified outliers (when existing) are plotted as
stars on the different figures proposed. The mean values are reported in the
tables and represented as “
Geographically, emissions are reported for the global scale, for three
latitudinal bands (< 30, 30–60,
60–90
Bottom-up estimates of methane emissions rely on models for individual processes (e.g. wetlands) or on inventories representing different source types (e.g. gas emissions). Chemistry transport models generally represent methane sinks individually in their chemical schemes (Williams et al., 2012). Therefore, it is possible to represent the bottom-up global methane budget for all individual sources. However, by construction, the total methane emissions derived from a combination of independent bottom-up estimates are not constrained.
For atmospheric inversions (top-down), the situation is different. Atmospheric
observations provide a constraint on the global source, given a fairly
strong constraint on the global sink derived using a proxy tracer such as
methyl chloroform (Montzka et al., 2011). 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 flux
categories (e.g. Bergamaschi et al., 2013). Indeed, the assimilation of
CH
In summary, bottom-up models and inventories are presented for all individual sources and for the five broad categories defined above at global scale, and only for four broad categories at regional scale. Top-down inversions are reported globally and regionally for the five broad categories of emissions.
Here we provide a complete review of all methane sources and sinks based on an ensemble of bottom-up approaches from multiple sources: process-based models, inventories, and data-driven methods. For each source, a description of the involved emitting process(es) is given, together with a brief description of the original datasets (measurements, models) and the related methodology. Then, the estimate for the global source and its range is given and analysed. Detailed descriptions of the datasets can be found elsewhere (see references of each component in the different subsections and tables).
Methane is emitted by a variety of sources in the atmosphere. These can be
sorted by emitting process (thermogenic, biogenic or pyrogenic) or by
anthropogenic vs. natural origin. Biogenic methane is the final product
of the decomposition of organic matter by
Various human activities lead to the emissions of methane to the atmosphere.
Agricultural processes under anaerobic conditions such as wetland rice
cultivation and livestock (enteric fermentation in animals, and the
decomposition of animal wastes) emit biogenic CH
Emission inventories were developed to generate bottom-up estimates of sector-specific emissions by compiling data on human activity levels and combining them with the associated emission factors.
An ensemble of individual inventories was gathered here to estimate anthropogenic methane emissions. We also refer to the extensive assessment report of the Arctic Monitoring and Assessment Programme (AMAP) published in 2015 on “Methane as Arctic climate forcer” (Höglund-Isaksson et al., 2015), which provides a detailed presentation and description of methane inventories and global scale estimates for the year 2005 (see their chap. 5 and in particular their Tables 5.1 to 5.5).
The main three bottom-up global inventories covering all anthropogenic
emissions are from the United States Environmental Protection Agency, USEPA (2012, 2006), the Greenhouse gas and Air pollutant Interactions and
Synergies (GAINS) model developed by the International Institute for Applied
Systems Analysis (IIASA) (Höglund-Isaksson, 2012) and the
Emissions Database for Global Atmospheric Research
(EDGARv4.1, 2010; EDGARv4.2FT2010, 2013). The
latter is an inventory compiled by the European Commission Joint Research
Centre (EC-JRC) and Netherland's Environmental Assessment Agency (PBL).
These inventories report the major sources of anthropogenic methane
emissions: fossil fuel production, transmission and distribution; livestock
(enteric fermentation and manure management); rice cultivation; solid waste
and waste water. However, the level of detail provided by country and by
sector varies between inventories, as these inventories do not consider the
same number of geographical regions and source sectors
(Höglund-Isaksson et al., 2015, see their Table 5.2). In
these inventories, methane emissions for a given region/country and a given
sector are usually calculated as the product of an activity level, an
emission factor for this activity and an abatement coefficient to account
for regulations implemented to control emissions if existing (see Eq. 5.1 of Höglund-Isaksson et al.,
2015; IPCC, 2006). The integrated emission models USEPA and
GAINS provide estimates every 5 or 10 years for both past and future
periods, while EDGAR provides annual estimates only for past emissions.
These datasets differ in their assumptions and the data used for the
calculation; however, they are not completely independent as they follow the
IPCC guidelines (IPCC, 2006). While the USEPA inventory adopts the emissions
reported by the countries to the UNFCCC, EDGAR and the GAINS model produced
their own estimates using a consistent approach for all countries. As a
result, the latter two approaches need large country-specific information
or, if not available, they adopt IPCC default factors or emission factors
reported to UNFCCC (Olivier et al., 2012;
Höglund-Isaksson, 2012). Here, we also integrate the Food and
Agriculture Organization (FAO) dataset, which provides estimates of methane
emissions at country level but only for agriculture (enteric fermentation,
manure management, rice cultivation, energy usage, burning of crop residues
and of savannahs) and land use (biomass burning) (FAO, 2016). It will
hereafter be referred as FAO-CH
Bottom-up models and inventories used in this study.
We use the following versions of these inventories: version EDGARv4.2FT2010
that provides yearly gridded emissions by sectors from 2000 to 2010
(Olivier and Janssens-Maenhout, 2012; EDGARv4.2FT2010,
2013), version 5a of the GAINS model (Höglund-Isaksson, 2012)
that assumes current legislation for air pollution for the future, the
revised estimates of 2012 from the USEPA (2012), and finally the
FAO emission database accessed in April 2016. Further details of the
inventories used in this study are provided in Table 1. Overall, only
EDGARv4.2FT2010 and GAINS provide gridded emission maps by sectors, and only
EDGAR provides gridded maps on a yearly basis, which explains why this
inventory is the most used in inverse modelling. These inventories are not
all regularly updated. For the purpose of this study, the estimates from
USEPA and GAINS have been linearly interpolated to provide yearly values, as
provided by the EDGAR inventory. We also use the EDGARv4.2FT2012 data, which
is an update of the time series of the country total emissions until 2012 (Rogelj et al., 2014; EDGARv4.2FT2012, 2014). This
update has been developed based on EDGARv4.2FT2010 and uses IEA energy
balance statistics (IEA, 2013) and NIR/CRF of UNFCCC (2013),
as described in part III of IEA's CO
Global methane emissions by source type in Tg CH
For this study, engaged before the update of EDGARv4.2 inventory up to 2012,
we built our own update from 2008 up to 2012 using FAO emissions to quantify
CH
Global anthropogenic methane emissions (excluding biomass burning)
from historical inventories and future projections (in
Tg CH
Based on the ensemble of inventories detailed above, anthropogenic emissions
are
Figure 2 presents the global methane emissions of anthropogenic sources
(excluding biomass and biofuel burning) estimated and projected by the
different inventories between 2000 and 2020. The inventories consistently
estimate that about 300 Tg of methane was released into the atmosphere in
2000 by anthropogenic activities. The main discrepancy between the
inventories is observed in their trend after 2005 with the lowest emissions
projected by USEPA and the largest emissions estimated by EDGARv4.2FT2012.
The increase in CH
Despite relatively good agreement between the inventories on total emissions from year 2000 onwards, large differences can be found at the sector and country levels (IPCC, 2014). Some of these discrepancies are detailed in the following sections.
For the fifth IPCC Assessment Report, four representative concentration
pathways (RCPs) were defined RCP8.5, RCP6, RCP4.5 and RCP2.6 (the latter is
also referred to as RCP3PD, where “PD” stands for peak and decline). The
numbers refer to the radiative forcing by the year 2100 in W m
Methane emissions from four source categories: natural wetlands, fossil fuels,
agriculture and waste, and biomass and biofuel burning for the
2003–2012 decade in mg CH
Most of the methane anthropogenic emissions related to fossil fuels come from the exploitation, transportation, and usage of coal, oil and natural gas. This geological and fossil type of emission (see natural source section) is driven by human activity. Additional emissions reported in this category include small industrial contributions such as production of chemicals and metals, and fossil fuel fires. Spatial distribution of methane emissions from fossil fuel is presented in Fig. 3 based on the mean gridded maps provided by EDGARv4.2FT2010 and GAINS over the 2003–2012 decade.
Global emissions of methane from fossil fuels and other industries are
estimated from three global inventories in the range of 114–133 Tg CH
During mining, methane is emitted from ventilation shafts, where large
volumes of air are pumped into the mine to keep methane at a rate below
0.5 % to avoid accidental inflammation. To prevent the diffusion of methane
in the mining working atmosphere, boreholes are made in order to evacuate
methane. In countries of the Organization for Economic Co-operation and
Development (OECD), methane recuperated from ventilation shafts is used as
fuel, but in many countries it is still emitted into the atmosphere or
flared, despite efforts for coal-mine recovery under the UNFCCC Clean
Development Mechanisms (
Almost 40 % (IEA, 2012) of the world's electricity is produced from coal. This contribution grew in the 2000s at the rate of several per cent per year, driven by Asian production where large reserves exist, but has stalled from 2011 to 2012. In 2012, the top 10 largest coal producing nations accounted for 88 % of total world emissions for coal mining. Among them, the top three producers (China, USA and India) produced two-thirds of the total (CIA, 2016).
Global estimates of methane emissions from coal mining show a large variation, in part due to the lack of comprehensive data from all major producing countries. The range of coal mining emissions is estimated at 18–46 Tg of methane for the year 2005, the highest value being from EDGARv4.2FT2010 and the lower from USEPA.
As announced in Sect. 3.1.2, coal mining is the main source explaining the
differences observed between inventories at global scale (Fig. 2). Indeed,
such differences are explained mainly by the different CH
For the 2003–2012 decade, methane emissions from coal mining are estimated
at 34 % of total fossil-fuel-related emissions of methane
(41 Tg CH
Natural gas is comprised primarily of methane, so any leaks during drilling
of the wells, extraction, transportation, storage, gas distribution, and
incomplete combustion of gas flares contribute to methane emissions (Lamb
et al., 2015; Shorter et al., 1996). Fugitive permanent 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, leakage can occur in gas transmission pipelines, due to
corrosion, manufacturing, welding, etc. According to
Lelieveld et al. (2005), the CH
Methane emissions from oil and natural gas systems also vary greatly in
different global inventories (46 to 98 Tg yr
For the 2003–2012 decade, methane emissions from upstream and downstream
natural oil and gas sectors are estimated to represent about 65 % of total
fossil CH
Production of natural gas from the exploitation of hitherto unproductive rock formations, especially shale, began in the 1980s in the US on an experimental or small-scale basis. Then, from early 2000s, exploitations started at large commercial scale. Two techniques developed and often applied together are horizontal drilling and hydraulic fracturing. The shale gas contribution to total natural gas production in the United States reached 40 % in 2012, growing rapidly from only small volumes produced before 2005 (EIA, 2015). Indeed, the practice of high-volume hydraulic fracturing (fracking) for oil and gas extraction is a growing sector of methane and other hydrocarbon production, especially in the US. Most recent studies (Miller et al., 2013; Moore et al., 2014; Olivier and Janssens-Maenhout, 2014; Jackson et al., 2014b; Howarth et al., 2011; Pétron et al., 2014; Karion et al., 2013) albeit not all (Allen et al., 2013; Cathles et al., 2012; Peischl et al., 2015) suggest that methane emissions are underestimated by inventories and agencies, including the USEPA. For instance, emissions in the Barnett Shale region of Texas from both bottom-up and top-down measurements showed that methane emissions from upstream oil and gas infrastructure were 90 % larger than estimates based on the USEPA's inventory and corresponded to 1.5 % of natural gas production (Zavala-Araiza et al., 2015). This study also showed that a few high emitters, neglected in the inventories, dominated emissions. Moreover these high emitting points, located on the conventional part of the facility, could be avoided through better operating conditions and repair of malfunctions. It also suggests that emission factor of conventional and non-conventional gas facilities might not be as different as originally thought (Howarth et al., 2011). Field measurements suggest that emission factors for unconventional gas are higher than for conventional gas, though the uncertainty, largely site-dependent, is large, ranging from small leakage rate of 1–2 % (Peischl et al., 2015) to widely spread rates of 3–17 % (Caulton et al., 2014; Schneising et al., 2014). For current technology, the GAINS model has adopted an emission factor of 4.3 % for shale-gas mining, still awaiting a clear consensus across studies.
This category includes methane emissions related to livestock (enteric
fermentation and manure), rice cultivation, landfills, and waste-water
handling. Of all types of emission, livestock is by far the largest emitter
of CH
Global emissions for agriculture and waste are estimated at 195 Tg CH
Domestic livestock such as cattle, buffalo, sheep, goats, and camels produce
a large amount of methane by anaerobic microbial activity in their digestive
systems (Johnson et al., 2002). A very stable temperature
(39
In addition, when livestock or poultry manure are stored or treated in
systems that promote anaerobic conditions (e.g. as a liquid/slurry in
lagoons, ponds, tanks, or pits), the decomposition of the volatile solids
component in the manure tends to produce CH
In 2005, global methane emissions from enteric fermentation and manure are
estimated in the range of 96–114 Tg CH
Here, for the 2003–2012 decade, based on all the databases aforementioned,
we infer a range of 97–111 Tg CH
This sector includes emissions from managed and non-managed landfills (solid waste disposal on land), and waste-water handling, where all kinds of waste are deposited, which can emit significant amounts of methane by anaerobic decomposition of organic material by microorganisms. Methane production from waste depends on pH, moisture and temperature. 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, 2010b).
Waste management is responsible for about 11 % of total global
anthropogenic methane emissions in 2000 at global scale (Kirschke et al.,
2013). A recent assessment of methane emissions in the US accounts landfills
for almost 26 % of total US anthropogenic methane emissions in 2014, the
largest contribution of any CH
Waste water from domestic and industrial sources is treated in municipal
sewage treatment facilities and private effluent treatment plants. The
principal factor in determining the CH
The inventories give robust emission estimates from solid waste in the range
of 28–44 Tg CH
In this study, global emissions of methane from landfills and waste are
estimated in the range of
52–63 Tg CH
Most of the world's rice is grown on flooded fields (Baicich,
2013). Under these shallow-flooded conditions, aerobic decomposition of
organic matter gradually depletes most of the oxygen in the soil, resulting
in anaerobic conditions under which methanogenic
The water management systems used to cultivate rice are one of the most
important factors influencing CH
The geographical distribution of the emissions is assessed by global (USEPA, 2006, 2012; EDGARv4.2FT2010, 2013) and regional (Peng et
al., 2016; Chen et al., 2013; Chen and Prinn, 2006; Yan et al., 2009;
Castelán-Ortega et al., 2014; Zhang et al., 2014) inventories or by land
surface models (Spahni et al., 2011; Zhang and Chen, 2014; Ren et al.,
2011; Tian et al., 2010, 2011; Li et al., 2005; Pathak et al.,
2005). The emissions show a seasonal cycle, peaking in the summer months in
the extratropics associated with the monsoon and land management. Similar
to emissions from livestock, emissions from rice paddies are influenced not
only by extent of rice field area (equivalent to the number of livestock)
but also by changes in the productivity of plants as these alter the
CH
The largest emissions are found in Asia (Hayashida et al., 2013), with China
(5–11 Tg CH
Based on global inventories only, global methane emissions from rice paddies
are estimated in the range 24–36 Tg CH
This category includes all the combustion processes: biomass (forests,
savannahs, grasslands, peats, agricultural residues) and biofuels in the
residential sector (stoves, boilers, fireplaces). Biomass and biofuel
burning emits methane under incomplete combustion conditions, when oxygen
availability is insufficient such as charcoal manufacture and smouldering
fires. The amount of methane that is emitted during the burning of biomass
depends primarily on the amount of biomass, the burning conditions, and the
material being burned. At the global scale, biomass and biofuel burning lead
to methane emissions of 27–35 Tg CH
In this study, we use the large-scale biomass burning (forest, savannah, grassland and peat fires) from specific biomass burning inventories and the biofuel burning contribution for the inventories (USEPA, GAINS and EDGAR).
The spatial distribution of methane emissions from biomass burning over the 2003–2012 decade is presented in Fig. 3 and is based on the mean gridded maps provided by EDGARv4.2FT2010 and GAINS for the biofuel burning, and based on the mean gridded maps provided by the biomass burning inventories presented thereafter.
Fire is the most important disturbance event in terrestrial ecosystems at the
global scale (van der Werf et al., 2010) and can be of either natural
(typically
Usually the biomass burning emissions are estimated using following Eq. (2)
(or similar):
The Global Fire Emission Database (GFED) is the most widely used global
biomass burning emission dataset and provides estimates from 1997. In this
review, we use both GFED3 (van der Werf et al., 2010) and GFED4s
(Giglio et al., 2013; Randerson et al., 2012). GFED is based on the
Carnegie–Ames–Stanford approach (CASA) biogeochemical model and satellite-derived estimates of burned area, fire activity and plant productivity. From
November 2000 onwards, these three parameters are inferred from the MODerate
resolution Imaging Spectroradiometer (MODIS) sensor. For the period prior to
MODIS, burned area maps were derived from the Tropical Rainfall Measuring
Mission (TRMM) Visible and Infrared Scanner (VIRS) and Along-Track Scanning
Radiometer (ATSR) active fire data and estimates of plant productivity
derived from Advanced Very High Resolution Radiometer (AVHRR) observations
during the same period. GFED3 has provided biomass burning emission
estimates from 1997 to 2011 at a 0.5
The Fire INventory from NCAR (FINN, Wiedinmyer et al., 2011) provides daily, 1km 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 2003–2014. FINNv1 uses MODIS satellite observations for active fires, land cover and vegetation density. The emission factors are from Akagi et al. (2011), the estimated fuel loading are assigned using model results from Hoelzemann et al. (2004), and the fraction of biomass burned is assigned as a function of tree cover (Wiedinmyer et al., 2006).
The Global Fire Assimilation System (GFAS, Kaiser
et al., 2012) calculates biomass burning emissions by assimilating Fire
Radiative Power (FRP) observations from MODIS at a daily frequency and
0.5
For FAO-CH
The differences in the biomass burning emission estimates arise from various
difficulties among them the ability to represent and know the geographical
and meteorological conditions and the fuel composition that highly impact
the combustion completeness and the emission factors. Also methane emission
factors vary greatly according to fire type, ranging from 2.2 g CH
Tian et al. (2016) estimated CH
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 burnt.
Although more than 2 billion people, mostly in developing and emerging
countries, use solid biofuels to cook and heat their homes on a daily basis
(André et al., 2014), methane emissions from biofuel
combustion have not yet received the attention it should have to estimate its
magnitude. Other much smaller contributors include agricultural burning
(
In this study, biofuel burning is estimated to contribute 12 Tg CH
Natural methane sources include wetland emissions as well as emissions from other land water systems (lakes, ponds, rivers, estuaries), land geological sources (seeps, microseepage, mud volcanoes, geothermal zones, and volcanoes, marine seepages), wild animals, wildfires, termites, terrestrial permafrost and oceanic sources (geological and biogenic). Many sources have been recognized but their magnitude and variability remain uncertain (USEPA, 2010a; Kirschke et al., 2013).
Wetlands are generally defined as ecosystems in which water saturation or inundation (permanent or not) dominates the soil development and determines the ecosystem composition (USEPA, 2010a). Such a broad definition needs to be refined when it comes to methane emissions. In this work, we define wetlands as ecosystems with inundated or saturated soils where anaerobic conditions lead to methane production (USEPA, 2010a; Matthews and Fung, 1987). This includes peatlands (bogs and fens), mineral wetlands (swamps and marshes), and seasonal or permanent floodplains. It excludes exposed water surfaces without emergent macrophytes, such as lakes, rivers, estuaries, ponds, and dams (addressed in the next section), as well as rice agriculture (see Sect. 3.1.4., rice cultivation paragraph). Even with this definition, one can consider that part of the wetlands could be considered as anthropogenic systems, being affected by human-driven land-use changes (Woodward et al., 2012). In the following we keep the generic denomination wetlands for natural and human-influenced wetlands.
A key feature of wetland systems producing methane is anaerobic soils, where
high water table or flooded conditions limit oxygen availability and create
conditions for methanogenesis. In anoxic conditions, organic matter can be
degraded by methanogens that produce CH
Land surface models estimate CH
In land surface models, wetland extent is either prescribed (from
inventories or remote-sensing data) or computed using hydrological models
accounting for the fraction of grid cell with flat topography prone to high
water table (e.g. Stocker et al., 2014; Kleinen et al., 2012), or from data assimilation against
remote-sensed observations (Riley et al., 2011). Mixed approaches can
also be implemented with tropical extent prescribed from remote sensing and
northern peatland extent explicitly computed (Melton et al., 2013).
Wetland extent appears to be a large contributor to uncertainties in methane
emissions from wetlands (Bohn et
al., 2015). For instance, the maximum wetland extent on a yearly basis
appeared to be very different among land surface models in Melton et al. (2013),
ranging from 7 to 27 Mkm
Integrated at the global scale, wetlands are the largest and most uncertain
source of methane to the atmosphere (Kirschke et al., 2013). An ensemble of
land surface models estimated the range of methane emissions of natural
wetlands at 141–264 Tg CH
In this work, following Melton et al. (2013),
11 land surface models (Table 1) computing net CH
The average emission map from wetlands for 2003–2012 built from the 11 models is plotted in Fig. 3. The zones with the largest emissions reflect the GLWD database: the Amazon basin, equatorial Africa and Asia, Canada, western Siberia, eastern India, and Bangladesh. Regions where methane emissions are robustly inferred (i.e. regions where mean flux is larger than the standard deviation of the models) represent 80 % of the total methane flux due to natural wetlands. Main primary emission zones are consistent between models, which is clearly favoured by the common wetland extend prescribed. But still, the different sensitivity of the models to temperature can generate substantial different patterns, such as in India. Some secondary (in magnitude) emission zones are also consistently inferred between models: Scandinavia, continental Europe, eastern Siberia, central USA, and tropical Africa. Using improved regional methane emission datasets (such as studies over North America, Africa, China, and Amazon) can enhance the accuracy of the global budget assessment (Tian et al., 2011; Xu and Tian, 2012; Ringeval et al., 2014; Valentini et al., 2014).
The resulting global flux range for natural wetland emissions is
153–227 Tg CH
This category includes methane emissions from freshwater systems (lakes,
ponds, rivers) and from brackish waters of estuaries. Methane emissions from
fresh waters and estuaries occur through a number of pathways including
(1) continuous or episodic diffusive flux across water surfaces,
(2) ebullition flux from sediments, (3) flux mediated through the
aerenchyma of emergent aquatic macrophytes (plant transport) in
littoral environments, and also for reservoirs, (4) degassing of CH
Freshwater contributions from lakes were first estimated to emit 1–20 Tg CH
Present data do not allow for separating inland water fluxes over the
different time periods investigated in this paper. The global estimates
provided are therefore assumed to be constant for this study. Here we
combine the latest estimates of global freshwater CH
Altogether, these studies consider data from more than 900 systems, of which
Potentially, the emissions from reservoirs should be allocated to
anthropogenic emissions (not done here). Regarding lakes and reservoirs,
tropical (< 30
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 exhalation and eruptions from onshore and shallow marine gas/oil seeps and through diffuse soil microseepage (after Etiope, 2015). Specifically, six source categories have been considered. Five are onshore sources: mud volcanoes (sedimentary volcanism), gas and oil seeps (independent of mud volcanism), microseepage (diffuse exhalation from soil in petroleum basins), geothermal (non-volcanic) manifestations and volcanoes. One source is offshore: submarine seepage (several types of gas manifestation at the seabed). Figure 4a shows the areas and locations potentially emitting geological methane, showing diffuse potential microseepage regions, macroseepage locations (oil–gas seeps, mud volcanoes) and geothermal/volcanic areas (built from Etiope, 2015), which represent more than 1000 emitting spots.
Studies since 2000 have shown that the natural release to the Earth's surface of methane of geological origin is an important global greenhouse gas source (Etiope and Klusman, 2002; Kvenvolden and Rogers, 2005; Etiope et al., 2008; USEPA, 2010a; Etiope, 2012, 2015). Indeed, the geological source is in the top-three natural methane sources after wetlands (and with freshwater systems) and about 10 % of total methane emissions, of the same magnitude or exceeding other sources or sinks, such as biomass burning, termites and soil uptake, considered in recent IPCC assessment reports (Ciais et al., 2013).
In this study, the following provided estimates were derived by bottom-up
approaches based on (a) the acquisition of thousands of land-based flux
measurements for various seepage types in many countries, and (b) the
application of the same procedures typically used for natural and
anthropogenic gas sources, following upscaling methods based on the
concepts of “point sources”, “area sources”, “activity” and “emission
factors”, as recommended by the air pollutant emission guidebook of the
European Environment Agency (EMEP/EEA, 2009). Our estimate is
consistent with a top-down global verification, based on observations of
radiocarbon-free (fossil) methane in the atmosphere (Etiope et al., 2008;
Lassey et al., 2007b), with a range of 33–75 Tg CH
As a result, in this study, the global geological methane emission is
estimated in the range of 35–76 Tg CH
Termites are important decomposer organisms, which play a very relevant role
in the cycling of nutrients in tropical and subtropical ecosystems
(Sanderson, 1996). The degradation of organic matter in their gut,
by symbiotic anaerobic microorganisms, leads to the production of CH
In this study, we adopt a value of 9 Tg CH
As for domestic ruminants, wild ruminants eruct or exhale methane through
the microbial fermentation process occurring in their rumen
(USEPA, 2010a). Global emissions of CH
The range adopted in this study is 2–15 Tg CH
Possible sources of oceanic CH
If the production of methane at seabed can be of importance, for instance,
marine seepages emit up to 65 Tg CH
All published estimates agree that contemporary global methane emissions
from oceanic sources are only a small contributor to the global methane
budget, but the range of estimates is relatively large from 1 to 35 Tg CH
More studies are needed to sort out this discrepancy and we choose to report
here the full range of 5–20 Tg CH
Concerning non-geological ocean emissions (biogenic, hydrates), the most
common value found in the literature is 10 Tg CH
Concerning more specifically atmospheric emissions from marine hydrates,
Etiope (2015) points that current estimates of methane air–sea flux
from hydrates (2–10 Tg CH
Overall, these elements suggest the necessity to revise to a lower value the
current total oceanic methane source to the atmosphere. Summing biogenic,
geological and hydrate emissions from oceans leads to a total oceanic
methane emission of 14 Tg CH
Permafrost is defined as frozen soil, sediment, or rock having temperatures
at or below 0
The thawing permafrost can generate direct and indirect methane emissions.
Direct methane emissions rely on the release of the methane contained in the
thawing permafrost. This flux to the atmosphere is small and estimated to be
at maximum 1 Tg CH
Here, we choose to report here only the direct emission range of 0–1 Tg CH
A series of recent studies define three distinct pathways for the production and emission of methane by living vegetation. First, plants produce methane through an abiotic photochemical process induced by stress (Keppler et al., 2006). This pathway was 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) meaning any potential implication for the global methane budget remains highly uncertain. Second, plants act as “straws”, drawing methane produced by microbes in anoxic soils (Rice et al., 2010; Cicerone and Shetter, 1981). Third, the stems of living trees commonly provide an environment suitable for microbial methanogenesis (Covey et al., 2012). Static chambers demonstrate locally significant through-bark flux from both soil-based (Pangala et al., 2013, 2015), and tree-stem-based methanogens (Wang et al., 2016). These studies indicate trees are a significant factor regulating ecosystem flux; however, estimates of biogenic plant-mediated methane emissions at broad scales are complicated by overlap with methane consumption in upland soil and production in wetlands. Integrating plant-mediated emissions in the global methane budget will require untangling these processes to better define the mechanisms, spatio-temporal patterns, and magnitude of these pathways.
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 is its oxidation by the hydroxyl
radical (OH), mostly in the troposphere, which contributes about 90 % of
the total methane sink (Ehhalt, 1974). Other losses are by
photochemistry in the stratosphere (reactions with chlorine atoms, Cl, and
atomic oxygen, O(
OH radicals are produced following the photolysis of ozone (O
OH concentrations and their changes can be sensitive to climate variability
(e.g. Pinatubo eruption, Dlugokencky et al., 1996), to
biomass burning (Voulgarakis et al., 2015) and to anthropogenic
activities. For instance, the recent increase of the oxidizing capacity of
the troposphere in South and East Asia, associated with increasing NO
We report here a climatological range of 454–617 Tg CH
Approximately 60 Tg CH
We report here a climatological range of 16–84 Tg CH
Halogen atoms can also contribute to the oxidation of methane in the
troposphere. Allan et al. (2005) measured mixing ratios of
methane and
We report here a climatological range of 13–37 Tg CH
Unsaturated oxic soils are sinks of atmospheric methane due to the presence
of methanotrophic bacteria, which consume methane as a source of energy.
Wetlands with temporally variable saturation can also act as methane sinks.
Dutaur and Verchot (2007) conducted a comprehensive meta-analysis of
field measurements of CH
Following Curry (2007), and consistent with Tian et al. (2015), we report here a climatological range of 9–47 Tg CH
The global atmospheric lifetime is defined for a gas in steady state as the
global atmospheric burden (Tg) of this gas divided by its global total sink
(Tg yr
The first systematic atmospheric CH
Four observational networks provide globally averaged CH
The networks differ in their sampling strategies, including the frequency of
observations, spatial distribution, and methods of calculating globally
averaged CH
In Fig. 1, (a) globally averaged CH
On decadal timescales, the annual increase is on average 2.1
In the 2000s, two space-borne instruments sensitive to atmospheric methane
were put in orbit and have provided atmospheric methane column-averaged dry
air mole fraction (XCH
Between 2003 and 2012, the Scanning Imaging Absorption spectrometer for
Atmospheric CartograpHY (SCIAMACHY) was operated on board the ESA
ENVIronmental SATellite (ENVISAT), providing nearly 10 years of XCH
In January 2009, the JAXA satellite Greenhouse Gases Observing SATellite
(GOSAT) was launched 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 (Yoshida et
al., 2013; Schepers et al., 2012; Parker et al., 2011) based on two
retrieval approaches: proxy and full physics. The proxy method retrieves the
ratio of methane column (XCH
Atmospheric inversions based on SCIAMACHY or GOSAT CH
The processes emitting methane discriminate differently its isotopologues
(isotopes). The two main stable isotopes of CH
Measurements of
Integrating isotopic information is important to improve our understanding
of the methane budget. Some studies have simulated such isotopic
observations (Neef et al., 2010; Monteil et al., 2011) or used them as
additional constraints to inverse systems (Mikaloff Fletcher et al.,
2004; Hein et al., 1997; Bousquet et al., 2006; Neef et al., 2010; Thompson
et al., 2015). Using pseudo-observations, Rigby et al. (2012)
found that quantum-cascade-laser-based isotopic observations would reduce
the uncertainty in four major source categories by about 10 % at the
global scale (microbial, biomass burning, landfill and fossil fuel) and by
up to 50 % at the local scale. Although all source types cannot be
separated using
Other types of methane measurements are available, which are not commonly used to infer fluxes from inverse modelling (yet) but are used to verify its performance (see e.g. Bergamaschi et al., 2013). Aircraft or balloon-borne in situ measurements can deliver vertical profiles with high vertical resolution. Such observations can also be used to test remote-sensing measurement from space or from the surface and bring them on the same scale as the in situ surface measurements. Aircraft measurements have been undertaken in various regions either during campaigns (Wofsy, 2011; Beck et al., 2012; Chang et al., 2014; Paris et al., 2010) or in a recurrent mode using small aircrafts in the planetary boundary layer (Sweeney et al., 2015; Umezawa et al., 2014; Gatti et al., 2014) and commercial aircrafts (Schuck et al., 2012; Brenninkmeijer et al., 2007; Umezawa et al., 2012, 2014; Machida et al., 2008). Balloons can carry in situ instruments (e.g. Joly et al., 2008; using tunable laser diode spectrometry) or air samplers (e.g. air cores, Karion et al., 2010) up to 30 km height. New technologies have also developed systems based on cavity ring-down spectroscopy (CRDS), opening a large ensemble of new activities to estimate methane emissions such as drone measurements (light version of CRDS), as land-based vehicles for real-time, mobile monitoring over oil and gas facilities, as well as ponds, landfills, livestock, etc.
In October 2006, the Infrared Atmospheric Sounding Interferometer (IASI) on board the European MetOp-A satellite began to operate. Measuring the thermal radiation from Earth and the atmosphere in the TIR, it provides mid-to-upper troposphere columns of methane (representative of the 5–15 km layer) over the tropics using an infrared sounding interferometer (Crevoisier et al., 2009). Despite its sensitivity being limited to the mid-to-upper troposphere, its use in flux inversions has shown consistent results in the tropics with surface and other satellite-based inversions (Cressot et al., 2014).
The Total Carbon Column Observing Network (TCCON) uses ground-based Fourier
transform spectrometers to measure atmospheric column abundances of
CO
Top-down studies used in this study with their contribution to the decadal and yearly estimates. For decadal means, top-down studies have to provide at least 6 years over the decade to contribute to the estimate. All top-down studies provided both total and per categories (including soil uptake) partitioning.
An atmospheric inversion for methane fluxes (sources and sinks) optimally
combines atmospheric observations of methane and associated uncertainties, a
prior knowledge of the fluxes including their uncertainties, and a
chemistry transport model to relate fluxes to concentrations (Rodgers,
2000). In this sense, top-down inversions integrate all the components of the
methane cycle described previously in this paper. The observations can be
surface or upper-air in situ observations, as well as satellite and surface retrievals.
Prior emissions generally come from bottom-up approaches such as process-based
models or data-driven extrapolations (natural sources) and inventories
(anthropogenic sources). The chemistry transport model can be Eulerian or
Lagrangian, and global or regional, depending on the scale of the flux to be
optimized. Atmospheric inversions generally rely on the Bayes' theorem, which
leads to the minimization of a cost function as Eq. (4):
A group of eight atmospheric inversion systems using global Eulerian
transport models were used in this synthesis. Each inversion system provides
from 1 to 10 inversions, including sensitivity tests varying the
assimilated observations (surface or satellite) or the inversion setup. This
represents a total of 30 inversion runs with different time coverage:
generally 2000–2012 for surface-based observations, 2003–2012 for
SCIAMACHY-based inversions and 2009–2012 for GOSAT-based inversions (Table 3). When multiple sensitivity tests were performed we use the mean of this
ensemble not to overweight one particular inverse model. Bias correction
procedures have been developed to assimilate SCIAMACHY (Bergamaschi et
al., 2009, 2013; Houweling et al., 2014) and GOSAT data (Cressot et al.,
2014; Houweling et al., 2014; Locatelli et al., 2015; Alexe et al., 2015).
These procedures can lead to corrections from several parts per billion and up to several
tens of parts per billion (Bergamaschi et al., 2009; Locatelli et al., 2015). Although
partly due to transport model errors, the large corrections applied to
satellite total column CH
General characteristics of the inversion systems are provided in Table 3. Further detail can be found in the referenced papers. Each group was asked to provide gridded flux estimates for the period 2000–2012, using either surface or satellite data, but no additional constraints were imposed so that each group could use their preferred inversion setup. This approach is appropriate for our purpose of flux assessment but not necessarily for model intercomparison. We did not require posterior uncertainty from the different participating groups, which may be done for the next release of the budget. Indeed chemistry transport models have some limitations that impact on the inferred methane budget, such as discrepancies in interhemispheric transport, stratospheric methane profiles and OH distribution. We consider here an ensemble of inversions gathering a large range of chemistry transport models, through their differences in vertical and horizontal resolutions, meteorological forcings, advection and convection schemes and boundary layer mixing; we assume that this model range is sufficient to cover the range of transport model errors in the estimate of methane fluxes. 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 were not analysed for this budget, which still relies on Kirschke et al. (2013) for bottom-up budget and on a global mass balance for top-down budget (difference between the global source and the observed atmospheric increase).
The last year of reported inversion results is 2012, which represents a 4-year lag with the present. Satellite observations are linked to operational data chains and are generally available within 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. With operational networks such as ICOS in Europe, these lags will be reduced in the future. In addition, the final 6 months of inversions are generally ignored (spun down) because the estimated fluxes are not constrained by as many observations as the previous months. Finally, the long inversion runs and analyses can take up to months to be performed. For the next global methane budget the objective is to represent more recent years by reducing the analysis time and shortening the in situ atmospheric observation release.
At the global scale, the total emissions inferred by the ensemble of 30
inversions are 558 Tg CH
The picture given by the bottom-up approaches is quite different with global
emissions of 736 Tg CH
Global, latitudinal and regional methane emissions in
Tg CH
The global methane budget for five source categories (see Sect. 2.3) for
2003–2012 is presented in Fig. 5 and Table 2. Top-down estimates attribute about
60 % of the total emissions to anthropogenic activities (range of
50–70 %) and 40 % to natural emissions. As natural emissions from bottom-up
models are much larger, the anthropogenic vs. natural emission ratio is
more balanced for bottom-up (
For 2003–2012, the top-down and bottom-up derived estimates of respectively
167 Tg CH
Methane global emissions from the five broad categories (see
Sect. 2.3) for the 2003–2012 decade for top-down inversions models (left light-coloured boxplots) in Tg CH
The discrepancy between top-down and bottom-up budgets is the largest for the natural
emission total, which is 384 Tg CH
Improved area estimates of freshwater emissions would be beneficial. For example, stream fluxes are difficult to assess because of the high-expected spatial variability and very uncertain areas of headwater streams where methane-rich groundwater may be rapidly degassed. There are also uncertainties in the geographical distinction between wetlands, small lakes (e.g. thermokarst lakes), and floodplains that will need more attention to avoid double counting. In addition, major uncertainty is still associated with representation of ebullition. The intrinsic nature of this large but very locally distributed flux highlights the need for cost-efficient high-resolution techniques for resolving the spatio-temporal variations of these fluxes. In this context of observational gaps in space and time, freshwater fluxes are considered underestimated until measurement techniques designed to properly account for ebullition become more common (Wik et al., 2016a). On the contrary, global estimates for freshwater emissions rely on upscaling of uncertain emission factors and emitting areas, with probable overlapping of wetland emissions (Kirschke et al., 2013), which may also lead to an overestimate. More work is needed, based on both observations and process modelling, to overcome these uncertainties.
For geological emissions, relatively large uncertainties come from the
extrapolation of only a subset of direct measurements to estimate the global
fluxes. Moreover, marine seepage emissions are still widely debated
(Berchet et al., 2016), and particularly
diffuse emissions from microseepage are highly uncertain. However, summing
up all fossil-CH
Total anthropogenic emissions are found statistically consistent between top-down
(328 Tg CH
Regional methane emissions for the 2003–2012 decade from top-down
inversions (grey) and for the prior estimates used in the inversions (white).
Each boxplot represents the range of the top-down estimates inferred by the
ensemble of inversion approach. Median value, and first and third quartiles are
presented in the box. The whiskers represent the minimum and maximum values
when suspected outliers are removed (see Sect. 2.2). Outliers are marked with
stars when existing. Mean values are represented with “
At regional scale, for the 2003–2012 decade (Table 4 and Fig. 6), total
methane emissions are dominated by Africa with 86 Tg CH
The different inversions assimilated either satellite- or ground-based
observations. It is of interest to determine whether these two types of data
provide consistent surface emissions. To do so, we computed global,
hemispheric and regional methane emissions using satellite-based inversions
and ground-based inversions separately for the 2010–2012 time period, which
is the longest time period for which results from both GOSAT satellite-based
and surface-based inversions were available. At the global scale,
satellite-based inversions infer significantly higher emissions (
Regional CH
The analysis of the regional methane budget per source category (Fig. 7) can
be performed both for bottom-up and top-down approaches but with limitations. A
complementary view of the methane budget is also available as an interactive
graphic produced using data visualization techniques (
Wetland emissions largely dominate methane emissions in tropical South
America, boreal North America, southern Africa, temperate South America and
South East Asia, although agriculture and waste emissions are almost as
important for the last two regions. Agriculture and waste emissions dominate
in India, China, contiguous USA, central North America, Europe and northern
Africa. Fossil fuel emissions dominate in Russia and are close to
agriculture and waste emissions in the region called central Eurasia and
Japan. In China, fossil fuel emissions are on average close, albeit smaller,
than agriculture and waste emissions. Comparison between bottom-up and top-down
approaches shows good consistency, but one has to consider the generally large
error bars, especially for top-down inversions. The largest discrepancy occurs
for wetland emissions in boreal North America where bottom-up models infer larger
emissions (32 Tg CH
Anthropogenic emissions remain close between top-down and bottom-up approaches for most
regions, again with the possibility that part of this agreement is due to
the lack of information brought by atmospheric observations to top-down
inversions for some regions. One noticeable exception is the lower emissions
for China as compared to the prior, visible also in Fig. 6. A priori
anthropogenic emissions for China are mostly provided by the EDGARv4.2
inventory. Starting from prior emissions of 67 Tg CH
In contrast to the Chinese estimates, emissions inferred for Africa and
especially southern Africa are significantly larger than in the prior
estimates (Fig. 6). For example, for southern Africa, the mean of the
inversion ensemble is 44 Tg CH
For all other regions, emission changes compared to prior values remain
within the first and third quartiles of the distributions. In particular,
contiguous USA (without Alaska) is found to emit 41 Tg CH
Kirschke et al. (2013) identified four main shortcomings in the assessment
of regional to global CH
Annual to decadal CH
The partitioning of CH
The ability to allocate observed atmospheric changes to changes of a given source is limited. Most inverse groups use EDGARv4.2 inventory as a prior, being the only
annual gridded anthropogenic inventory to date. An updated version of the
EDGARv4.2 inventory has been recently released (EDGARv4.2FT2012), which is
very close at a global scale to the extrapolation performed in this paper
based on statistics from BP (
Uncertainties in the modelling of atmospheric transport and chemistry limit the optimal assimilation of atmospheric
observations and increase the uncertainties of the inversion-derived flux estimates. In this work, we gathered more inversion
models than in Kirschke et al. (2013), leading to small to significant regional differences in the methane
budget for 2000–2009. For the next release, it is important to stabilize the
core group of participating inversions in order not to create artificial
changes in the reporting of uncertainties. More, the recent results of
Locatelli et al. (2015), who studied the sensitivity of
inversion results to the representation of atmospheric transport, suggest
that regional changes in the balance of methane emissions between inversions
may be due to different characteristics of the transport models used here as
compared to Kirschke et al. (2013). Indeed, the TRANSCOM experiment
synthesized in Patra et al. (2011) showed a large sensitivity of the
representation of atmospheric transport on methane concentrations in the
atmosphere. As an illustration, in their study, the modelled CH
We have built a global methane budget by gathering and synthesizing a large
ensemble of published results using a consistent methodology, including
atmospheric observations and inversions (top-down inversions), process-based
models for land surface emissions and atmospheric chemistry, and inventories
of anthropogenic emissions (bottom-up models and inventories). For the 2003–2012
decade, global methane emissions are 558 Tg CH
The latitudinal breakdown inferred from top-down approaches reveals a domination
of tropical emissions (
Our results, including an extended set of inversions, are compared with the former synthesis of Kirschke et al. (2013), showing good consistency overall when comparing the same decade (2000–2009) at the global scale. Significant differences occur at the regional scale when comparing the 2000–2009 decadal emissions. This important result indicates that using different transport models and inversion setups can significantly change the partition of emissions at the regional scale, making it less robust. It also means that we need to gather a stable, and as complete as possible, core of transport models in the next release of the budget in order to integrate this uncertainty within the budget.
Among the different uncertainties raised in Kirschke et al. (2013), the
present work 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. The magnitudes and uncertainties of all other
natural sources have been revised and updated, which has led to decreased the
emission estimates for oceans, termites, wild animals and wildfires, and to
increased emission estimates and range for freshwater systems. Although the
risk of double counting emissions between natural and anthropogenic gas
leaks exists, total fossil-related reported emissions are found consistent
with atmospheric
On the top of the decadal methane budget presented in this paper, trends and
year-to-year changes in the methane cycle have been highly discussed in the
recent literature, especially because a sustained atmospheric positive
growth rate of more than
The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights on the methane budget and its changes and help to reduce the uncertainties in the methane budget. 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, or if the conclusion or results depend on the data, co-authorship may need to 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 produce the regional budget.
The file Global_Methane_Budget_2000-2012_v1.1.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 (excluding biomass burning emissions), used to produce Fig. 2, (4) the global and regional budgets over 2000–2009 based on bottom-up approaches, (5) the global and regional budgets over 2000–2009 based on top-down approaches, (6) the global and regional budgets over 2003–2012 based on bottom-up approaches, (7) the global and regional budgets over 2003–2012 based on top-down approaches, (8) the global and regional budgets for year 2012 based on bottom-up approaches, (9) the global and regional budgets for year 2012 based on top-down approaches, and (10) the list of contributors to contact for further information on specific data.
This database is available from the Carbon Dioxide Information Analysis
Center (Saunois et al., 2016) and the Global Carbon
Project (
This collaborative international effort is part of the Global Carbon Project activity to establish and track greenhouse gas budgets and their trends. Fortunat Joos and Renato Spahni acknowledge support by the Swiss National Science Foundation. Heon-Sook Kim and Shamil Maksyutov acknowledge use of the GOSAT Research Computation Facility. Donald R. Blake and Isobel J. Simpson (UCI) acknowledge funding support from NASA. Josep G. Canadell thanks the support from the National Environmental Science Program – Earth Systems and Climate Change Hub. Marielle Saunois and Philippe Bousquet acknowledge the Global Carbon Project for the scientific advice and the computing power of LSCE for data analyses. Peter Bergamaschi and Mihai Alexe acknowledge the support by the European Commission Seventh Framework Programme (FP7/2007–2013) project MACC-II under grant agreement 283576, by the European Commission Horizon2020 Programme project MACC-III under grant agreement 633080, and by the ESA Climate Change Initiative Greenhouse Gases Phase 2 project. William J. Riley and Xiyan Xu acknowledge support by the US Department of Energy, BER, under contract no. DE-AC02-05CH11231. The FAOSTAT database is supported by regular programme funding from all FAO member countries. Prabir K. Patra is supported by the Environment Research and Technology Development Fund (2-1502) of the Ministry of the Environment, Japan. David J. Beerling acknowledges support from an ERC Advanced grant (CDREG, 322998) and NERC (NE/J00748X/1). David Bastviken and Patrick Crill acknowledge support from the Swedish Research Council VR. Glen P. Peters acknowledges the support of the Research Council of Norway project 244074. Hanqin Tian and Bowen Zhang acknowledge funding support from NASA (NNX14AF93G; NNX14AO73G) and NSF (1243232; 1243220). Changhui Peng acknowledges the support by National Science and Engineering Research Council of Canada (NSERC) discovery grant and China's QianRen Program. The CSIRO and the Australian Government Bureau of Meteorology are thanked for their ongoing long-term support of the Cape Grim station and the Cape Grim science programme. The CSIRO flask network is supported by CSIRO Australia, Australian Bureau of Meteorology, Australian Institute of Marine Science, Australian Antarctic Division, NOAA USA, and the Meteorological Service of Canada. The operation of the AGAGE instruments at Mace Head, Trinidad Head, Cape Matatula, Ragged Point, and Cape Grim is supported by the National Aeronautic and Space Administration (NASA) (grants NAG5-12669, NNX07AE89G, and NNX11AF17G to MIT and grants NNX07AE87G, NNX07AF09G, NNX11AF15G, and NNX11AF16G to SIO), the Department of Energy and Climate Change (DECC, UK) contract GA01081 to the University of Bristol, and the Commonwealth Scientific and Industrial Research Organization (CSIRO Australia), and Bureau of Meteorology (Australia). Nicola Gedney and Andy Wiltshire acknowledge support by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101).
Marielle Saunois and Philippe Bousquet acknowledge Lyla Taylor (University of Sheffield, UK), Chris Jones (Met Office, UK) and Charlie Koven (Lawrence Berkeley National Laboratory, USA) for their participation to land surface modelling of wetland emissions. Marielle Saunois, Philippe Bousquet, and Theodore J. Bohn (ASU, USA), Jens Greinhert (GEOMAR, the Netherlands), Charles Miller (JPL, USA), and Tonatiuh Guillermo Nunez Ramirez (MPI Jena, Germany) are thanked for their useful comments and suggestions on the manuscript. Marielle Saunois and Philippe Bousquet acknowledge Martin Herold (WU, the Netherlands), Mario Herrero (CSIRO, Australia), Paul Palmer (University of Edinburgh, UK), Matthew Rigby (University of Bristol, UK), Taku Umezawa (NIES, Japan), Ray Wang (GIT, USA), Jim White (INSTAAR, USA), Tatsuya Yokota (NIES, Japan), Ayyoob Sharifi and Yoshiki Yamagata (NIES/GCP, Japan) and Lingxi Zhou (CMA, China) for their interest and discussions on the Global Carbon project methane. Finally, Marielle Saunois and Philippe Bousquet are grateful to Cathy Nangini and Patrick Brockmann of the LSCE Data Visualization Group for their help with the design and production of the interactive data visualization. Edited by: D. Carlson Reviewed by: E. Nisbet and one anonymous referee