Understanding and quantifying the global methane (
For the 2008–2017 decade, global methane emissions are estimated by
atmospheric inversions (a top-down approach) to be 576 Tg
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
The data presented here can be downloaded from
The surface dry air mole fraction of atmospheric methane (
Globally averaged atmospheric
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
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
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
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 (
Kirschke et al. (2013) were the first to conduct a
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.
Unless specified, fluxes are expressed in teragrams of
The
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.
Geographically, emissions are reported globally and for three latitudinal
bands (90
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
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
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
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.
Bottom-up models and inventories for anthropogenic and biomass burning
estimates used in this study.
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
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 “
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.
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
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-
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-
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-
Based on the ensemble of databases detailed above, total anthropogenic
emissions were 366 [349–393] Tg
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
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
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
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
During mining, methane is emitted primarily from ventilation shafts, where
large volumes of air are pumped into the mine to keep the
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
Global estimates of
For the 2008–2017 decade, methane emissions from coal mining represent
33 % of total fossil-fuel-related emissions of methane (42 Tg
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),
Methane emissions from oil and natural gas systems vary greatly in different
global inventories (72 to 97 Tg yr
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
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
Global emissions from agriculture and waste for the period 2008–2017 are
estimated to be 206 Tg
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
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 (
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
Global methane emissions from enteric fermentation and manure management are
estimated in the range of 99–115 Tg
For the period 2008–2017, we estimated total emissions of 111 [106–116] Tg
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
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
The largest emissions from rice cultivation are found in Asia, accounting for
30 % to 50 % of global emissions (Fig. 3). The decrease in
Based on the global inventories considered in this study, global methane
emissions from rice paddies are estimated to be 30 [25–38] Tg
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
Wastewater from domestic and industrial sources is treated in municipal
sewage treatment facilities and private effluent treatment plants. The
principal factor in determining the
The GAINS model and CEDS and EDGAR inventories give robust emission
estimates from solid waste in the range of 29–41 Tg
In our study, the global emission of methane from waste management is
estimated in the range of 60–69 Tg
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
Fire is an important disturbance event in terrestrial
ecosystems globally (van
der Werf et al., 2010) and can be of either natural (typically
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
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
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
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
The FAO-
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
In this study, based on the five aforementioned products, biomass burning
emissions are estimated at 17 Tg
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
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
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
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
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
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).
In this work, 13 land surface models computing net
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
For the last decade, 2008–2017, the average ensemble emissions were 149 Tg
Global methane emissions by source type (Tg
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
Methane emissions from lakes were first estimated to be
1–20 Tg
A regional estimate for latitudes above 50
On top of emissions pathways described for inland waters,
reservoirs have specific ones including degassing of
Combining
emissions from lakes and ponds from Bastviken et al. (2011) (71.6 Tg
Previously, Kirschke et al. (2013)
reported a range of 8–73 Tg
Methane emissions (mg
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
In this budget, we report a mean value of 159 Tg
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
While all bottom-up and some top-down estimates, following different and
independent techniques from different authors, consistently suggest a global
geo-
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
Termites are an infraorder of insects (Isoptera), which occur predominantly
in the tropical and subtropical latitudes (Abe et al.,
2000).
In Kirschke et al. (2013)
(see their Supplement), a re-analysis of
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
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
In this study, we report a decadal value of 9 Tg
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
Based on these findings, the range adopted in this updated methane budget is
2 [1–3] Tg
Oceanic sources comprise coastal ocean and open ocean methane release.
Possible sources of oceanic
The most common biogenic
ocean emission value found in the literature is 10 Tg
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
As a result, here we report a range of 4–10 Tg
The production of methane at the seabed is known to be
significant. For instance, marine seepages emit up to 65 Tg
For geological emissions, the most used value has long been 20 Tg
Therefore, as discussed in Sect. 3.2.2, we report here a reduced range of
5–10 Tg
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
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
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
Methane emissions from brackish water were not estimated in Saunois et al. (2016)
and an additional 4 Tg
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 methane contained in the
thawing permafrost. This flux to the atmosphere is small and estimated to be
a maximum of 1 Tg
Here, we choose to report only the direct emission range of 0–1 Tg
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
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 (
OH radicals are produced following the photolysis of ozone (
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
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
We report here a climatological range for the tropospheric loss of methane
by OH oxidation of 553 [476–677] Tg
In the stratosphere,
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(
We report here a climatological range of 12–37 Tg
Halogen atoms can also contribute to the oxidation of methane in the
troposphere. Allan et al. (2005) measured mixing
ratios of methane and
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
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
The previous Curry (2007) estimate can be revised
upward based on subsequent work and the increase in
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
Systematic atmospheric
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.
We use globally averaged
The networks differ in their sampling strategies, including the frequency of
observations, spatial distribution, and methods of calculating globally
averaged
In Fig. 1, (a) globally averaged
From 1999 to 2006, the annual increase in atmospheric
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
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).
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.
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.
At the global scale, the total emissions inferred by the
ensemble of 22 inversions is 576 Tg
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
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
Global methane budget for the 2008–2017 decade. Both bottom-up
(left) and top-down (right) estimates (Tg
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
For wetlands in 2008–2017, the top-down and bottom-up estimates of 181 Tg
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
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-
Total anthropogenic emissions for the period
2008–2017 were assessed to be statistically consistent between top-down (359 Tg
For top-down estimates, the
For the bottom-up estimates, the total chemical loss for the 2000s reported here is
595 Tg
The latitudinal breakdown of emissions inferred from atmospheric inversions
reveals a dominance of tropical emissions at 368 Tg
Global and latitudinal total methane emissions (Tg
Over 2010–2017, at the global scale, satellite-based inversions infer almost
identical emissions to ground-based inversions (difference of 3 [0–7] Tg
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
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
Latitudinal methane emissions in teragrams of
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
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).
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.
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
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- 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-
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-
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
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 Further study the reactivity of the air parcels in the chemistry–climate
models and define new diagnostics to assess modelled 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
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 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 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. 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 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
Over the long run, integrating more measurements and regional studies will
help to improve the top-down systems, and further reduce the uncertainties.
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
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.
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 (
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
The latitudinal breakdown inferred from top-down approaches reveals a
dominant role of tropical 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
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
(
In addition to the decadal
Funding supporting the production of the various components of the global methane budget in addition to the authors' supporting institutions (see also Acknowledgements).
Continued.
A former version of this article was published on 12 December 2016 and is available at
The supplement related to this article is available online at:
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.
The authors declare that they have no conflict of interest.
The views expressed in this publication are those of the author(s) and do not necessarily reflect the views or policies of FAO.
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.
Please see a full list of funders in the Appendix (Table A1).
This paper was edited by David Carlson and reviewed by Michael Prather and one anonymous referee.