Tg CH4 Yr Research Articles

Global Climate Change Increases Terrestrial Soil CH4 Emissions

AbstractIncreased greenhouse gas emissions are causing unprecedented climate change, which is, in turn, altering emissions and removals (referring to the oxidation of atmospheric CH4 by methanotrophs within the soil) of the atmospheric CH4 in terrestrial ecosystems. In the global CH4 budget, wetlands are the dominant natural source and upland soils are the primary biological sink. However, it is unclear whether and how the soil CH4 exchanges across terrestrial ecosystems and the atmosphere will be affected by warming and changes in precipitation patterns. Here, we synthesize 762 observations of in situ soil CH4 flux data based on the chamber method from the past three decades related to temperature and precipitation changes across major terrestrial ecosystems worldwide. Our meta‐analysis reveals that warming (average warming of +2°C) promotes upland soil CH4 uptake and wetland soil CH4 emission. Decreased precipitation (ranging from −100% to −7% of local mean annual precipitation) stimulates upland soil CH4 uptake. Increased precipitation (ranging from +4% to +94% of local mean annual precipitation) accelerates the upland soil CH4 emission. By 2100, under the shared socioeconomic pathway with a high radiative forcing level (SSP585), CH4 emissions from global terrestrial ecosystems will increase by 22.8 ± 3.6 Tg CH4 yr−1 as an additional CH4 source, which may be mainly attributed to the increase in precipitation over 30°N latitudes. Our meta‐analysis strongly suggests that future climate change would weaken the natural buffering ability of terrestrial ecosystems on CH4 fluxes and thus contributes to a positive feedback spiral.

Wetland emission and atmospheric sink changes explain methane growth in 2020.

Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns1. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr-1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr-1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr-1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide(NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns2. From atmospheric inversions,we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr-1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr-1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings3,4, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism inthe future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge5.

Quantifying Methane Emissions from Aquaculture Ponds in China.

Small ponds are important methane (CH4) sources. However, current estimates of CH4 emissions from aquaculture ponds are largely uncertain due to data paucity, especially in China─the largest aquaculture producer in the world. Here, we present a nationwide metadata analysis with a database of 55 field observations to examine total CH4 emissions from aquaculture ponds in China. We found that the annual CH4 fluxes from aquaculture ponds are much larger than those from reservoirs and lakes. The total CH4 emission from aquaculture ponds is 1.60 ± 0.62 Tg CH4 yr-1, with an average growth rate of ∼0.03 Tg CH4 yr-2 during the period 2008-2019. Compared with global major protein-producing livestocks, aquaculture species have a lower (63%) emission intensity, defined by the amount of CH4 emitted per unit of animal proteins. Our study highlights the essential contribution of China's aquaculture ponds to national CH4 emissions and the lower environmental cost of the aquaculture sector for future animal protein production. More field measurements with multi-scale observations are urgently needed to reduce the uncertainty of CH4 emissions from aquaculture ponds.

How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions?

Abstract. Atmospheric methane (CH4) concentrations have been rising since 2007 due to an imbalance between CH4 sources and sinks. The CH4 budget is generally estimated through top-down approaches using chemistry transport models (CTMs) and CH4 observations as constraints. The atmospheric isotopic CH4 composition, δ13C(CH4), can also provide additional constraints and helps to discriminate between emission categories. Nevertheless, to be able to use the information contained in these observations, the models must correctly account for processes influencing δ13C(CH4). The oxidation by chlorine (Cl) likely contributes less than 5 % to the total oxidation of atmospheric CH4. However, the large kinetic isotope effect of the Cl sink produces a large fractionation of 13C, compared with 12C in atmospheric CH4, and thus may strongly influence δ13C(CH4). When integrating the Cl sink in their setup to constrain the CH4 budget, which is not yet standard, atmospheric inversions prescribe different Cl fields, therefore leading to discrepancies between flux estimates. To quantify the influence of the Cl concentrations on CH4, δ13C(CH4), and CH4 budget estimates, we perform sensitivity simulations using four different Cl fields. We also test removing the tropospheric and the entire Cl sink. We find that the Cl fields tested here are responsible for between 0.3 % and 8.5 % of the total chemical CH4 sink in the troposphere and between 1.0 % and 1.6 % in the stratosphere. Prescribing these different Cl amounts in atmospheric inversions can lead to differences of up to 53.8 Tg CH4 yr−1 in global CH4 emissions and of up to 4.7 ‰ in the globally averaged isotopic signature of the CH4 source δ13C(CH4)source), although these differences are much smaller if only recent Cl fields are used. More specifically, each increase by 1000 molec.cm-3 in the mean tropospheric Cl concentration would result in an adjustment by +11.7 Tg CH4 yr−1, for global CH4 emissions, and −1.0 ‰, for the globally averaged δ13C(CH4)source. Our study also shows that the CH4 seasonal cycle amplitude is modified by less than 1 %–2 %, but the δ13C(CH4) seasonal cycle amplitude can be significantly modified by up to 10 %–20 %, depending on the latitude. In an atmospheric inversion performed with isotopic constraints, this influence can result in significant differences in the posterior source mixture. For example, the contribution from wetland emissions to the total emissions can be modified by about 0.8 % to adjust the globally averaged δ13C(CH4)source, corresponding to a 15 Tg CH4 yr−1 change. This adjustment is small compared to the current wetland source uncertainty, albeit far from negligible. Finally, tested Cl concentrations have a large influence on the simulated δ13C(CH4) vertical profiles above 30 km and a very small impact on the simulated CH4 vertical profiles. Overall, our model captures the observed CH4 and δ13C(CH4) vertical profiles well, especially in the troposphere, and it is difficult to prefer one Cl field over another based uniquely on the available observations of the vertical profiles.

Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use

The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO2 emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH4 yr−1) of the methane emission growth rate of China (0.87 Tg CH4 yr−1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Paddy rice methane emissions across Monsoon Asia

Although rice cultivation is one of the most important agricultural sources of methane (CH4) and contributes ∼8% of total global anthropogenic emissions, large discrepancies remain among estimates of global CH4 emissions from rice cultivation (ranging from 18 to 115 Tg CH4 yr−1) due to a lack of observational constraints. The spatial distribution of paddy-rice emissions has been assessed at regional-to-global scales by bottom-up inventories and land surface models over coarse spatial resolution (e.g., > 0.5°) or spatial units (e.g., agro-ecological zones). However, high-resolution CH4 flux estimates capable of capturing the effects of local climate and management practices on emissions, as well as replicating in situ data, remain challenging to produce because of the scarcity of high-resolution maps of paddy-rice and insufficient understanding of CH4 predictors. Here, we combine paddy-rice methane-flux data from 23 global eddy covariance sites and MODIS remote sensing data with machine learning to 1) evaluate data-driven model performance and variable importance for predicting rice CH4 fluxes; and 2) produce gridded up-scaling estimates of rice CH4 emissions at 5000-m resolution across Monsoon Asia, where ∼87% of global rice area is cultivated and ∼ 90% of global rice production occurs. Our random-forest model achieved Nash-Sutcliffe Efficiency values of 0.59 and 0.69 for 8-day CH4 fluxes and site mean CH4 fluxes respectively, with land surface temperature, biomass and water-availability-related indices as the most important predictors. We estimate the average annual (winter fallow season excluded) paddy rice CH4 emissions throughout Monsoon Asia to be 20.6 ± 1.1 Tg yr−1 for 2001–2015, which is at the lower range of previous inventory-based estimates (20–32 CH4 Tg yr−1). Our estimates also suggest that CH4 emissions from paddy rice in this region have been declining from 2007 through 2015 following declines in both paddy-rice growing area and emission rates per unit area, suggesting that CH4 emissions from paddy rice in Monsoon Asia have likely not contributed to the renewed growth of atmospheric CH4 in recent years.

Global estimates of forest soil methane flux identify a temperate and tropical forest methane sink

Forest ecosystems play an important role in the global CH4 cycle. Understanding and quantifying the contribution and distribution of CH4 sinks and sources in global forest soils is vital for assessing realistic approaches to climate change mitigation. Here, we compiled a dataset of in situ global forest soil CH4 fluxes from published data, incorporating 772 case studies covering boreal (n = 12), temperate (n = 369), subtropical (n = 208), and tropical (n = 183) forests and spanning 1991–2020 as a basis to build the mixed-effect model. Using the screened best model, we identified the main drivers and predicted the global distribution of the forest soil CH4 flux. Our research revealed that global forest soil CH4 uptake decreased significantly with increasing mean annual temperature (MAT), soil bulk density (BD), soil organic carbon (SOC), and soil total nitrogen (TN) but increased significantly with increasing mean annual precipitation (MAP). The global mean CH4 uptake rate in forest soils was 3.95 ± 1.78 kg CH4 ha−1 yr−1, with the total sink of 14.98 ± 6.75 Tg CH4 yr−1. The soil CH4 sinks in temperate and tropical forests contributed 84 % to the total sink of global forests. The CH4 emission rate in global forest soils averaged 1.12 ± 1.11 kg CH4 ha−1 yr−1, with the total source of 0.14 ± 0.14 Tg CH4 yr−1. Nearly 3 % of the total area of global forest soils was a net CH4 source. In summary, we identified the key drivers of forest soil CH4 flux and improved previous estimates of the global CH4 budget in forest soils. These findings can support decision-making related to forest management and greenhouse gas restrictions.

Dairy Manure Co-composting with Wood Biochar Plays a Critical Role in Meeting Global Methane Goals.

Livestock are the largest source of anthropogenic methane (CH4) emissions, and in intensive dairy systems, manure management can contribute half of livestock CH4. Recent policies such as California’s short-lived climate pollutant reduction law (SB 1383) and the Global Methane Pledge call for cuts to livestock CH4 by 2030. However, investments in CH4 reduction strategies are primarily aimed at liquid dairy manure, whereas stockpiled solids remain a large source of CH4. Here, we measure the CH4 and net greenhouse gas reduction potential of dairy manure biochar-composting, a novel manure management strategy, through a composting experiment and life-cycle analysis. We found that biochar-composting reduces CH4 by 79%, compared to composting without biochar. In addition to reducing CH4 during composting, we show that the added climate benefit from biochar production and application contributes to a substantially reduced life-cycle global warming potential for biochar-composting: −535 kg CO2e Mg–1 manure compared to −194 kg CO2e Mg–1 for composting and 102 kg CO2e Mg–1 for stockpiling. If biochar-composting replaces manure stockpiling and complements anaerobic digestion, California could meet SB 1383 with 132 less digesters. When scaled up globally, biochar-composting could mitigate 1.59 Tg CH4 yr–1 while doubling the climate change mitigation potential from dairy manure management.

Methane Emission From Global Lakes: New Spatiotemporal Data and Observation-Driven Modeling of Methane Dynamics Indicates Lower Emissions.

Lakes have been highlighted as one of the largest natural sources of the greenhouse gas methane (CH4) to the atmosphere. However, global estimates of lake CH4 fluxes over the last 20 years exhibit widely different results ranging from 6 to 185 Tg CH4 yr−1, which is to a large extent driven by differences in lake areas and thaw season lengths used. This has generated uncertainty regarding both lake fluxes and the global CH4 budget. This study constrains global lake water CH4 emissions by using new information on lake area and distribution and CH4 fluxes distinguished by major emission pathways; ecoclimatic lake type; satellite‐derived ice‐free emission period length; and diel‐ and temperature‐related seasonal flux corrections. We produced gridded data sets at 0.25° latitude × 0.25° longitude spatial resolution, representing daily emission estimates over a full annual climatological cycle, appropriate for use in global CH4 budget estimates, climate and Earth System Models, bottom‐up biogeochemical models, and top‐down inverse model simulations. Global lake CH4 fluxes are 41.6 ± 18.3 Tg CH4 yr−1 with approximately 50% of the flux contributed by tropical/subtropical lakes. Strong temperature‐dependent flux seasonality and satellite‐derived freeze/thaw dynamics limit emissions at high latitudes. The primary emission pathway for global annual lake fluxes is ebullition (23.4 Tg) followed by diffusion (14.1 Tg), ice‐out and spring water‐column turnover (3.1 Tg), and fall water‐column turnover (1.0 Tg). These results represent a major contribution to reconciling differences between bottom‐up and top‐town estimates of inland aquatic system emissions in the global CH4 budget.

Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate <i>δ</i><sup>13</sup>C(CH<sub>4</sub>) and CH<sub>4</sub>: a case study with model LMDz-SACS

Abstract. Atmospheric CH4 mole fractions resumed their increase in 2007 after a plateau during the 1999–2006 period, indicating relative changes in the sources and sinks. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is, nevertheless, challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attributing these changes to specific source variations. δ13C(CH4)source isotopic signatures of CH4 sources differ between emission categories (biogenic, thermogenic, and pyrogenic) and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both the emissions and the associated source signatures of multiple emission categories at the pixel scale. To our knowledge, this represents the first attempt to carry out variational inversion assimilating δ13C(CH4) with a 3-D chemistry transport model (CTM) and to independently optimize isotopic source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system and analyze how sensitive the system is to the setup controlling the optimization using the LMDz-SACS 3-D CTM. We find that assimilating δ13C(CH4) observations and allowing the system to adjust isotopic source signatures provide relatively large differences in global flux estimates for wetlands (−5.7 Tg CH4 yr−1), agriculture and waste (−6.4 Tg CH4 yr−1), fossil fuels (+8.6 Tg CH4 yr−1) and biofuels–biomass burning (+3.2 Tg CH4 yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming that the source signatures are perfectly known, these differences increase by a factor of 3–4, strengthening the importance of having as accurate signature estimates as possible. Initial conditions, uncertainties in δ13C(CH4) observations, or the number of optimized categories have a much smaller impact (less than 2 Tg CH4 yr−1).

A 130‐year global inventory of methane emissions from livestock: Trends, patterns, and drivers

Livestock contributes approximately one-third of global anthropogenic methane (CH4 ) emissions. Quantifying the spatial and temporal variations of these emissions is crucial for climate change mitigation. Although country-level information is reported regularly through national inventories and global databases, spatially explicit quantification of century-long dynamics of CH4 emissions from livestock has been poorly investigated. Using the Tier 2 method adopted from the 2019 Refinement to 2006 IPCC guidelines, we estimated CH4 emissions from global livestock at a spatial resolution of 0.083° (~9km at the equator) during the period 1890-2019. We find that global CH4 emissions from livestock increased from 31.8 [26.5-37.1] (mean [minimum-maximum of 95% confidence interval) Tg CH4 yr-1 in 1890 to 131.7 [109.6-153.7] Tg CH4 yr-1 in 2019, a fourfold increase in the past 130 years. The growth in global CH4 emissions mostly occurred after 1950 and was mainly attributed to the cattle sector. Our estimate shows faster growth in livestock CH4 emissions as compared to the previous Tier 1 estimates and is ~20% higher than the estimate from FAOSTAT for the year 2019. Regionally, South Asia, Brazil, North Africa, China, the United States, Western Europe, and Equatorial Africa shared the majority of the global emissions in the 2010s. South Asia, tropical Africa, and Brazil have dominated the growth in global CH4 emissions from livestock in the recent three decades. Changes in livestock CH4 emissions were primarily associated with changes in population and national income and were also affected by the policy, diet shifts, livestock productivity improvement, and international trade. The new geospatial information on the magnitude and trends of livestock CH4 emissions identifies emission hotspots and spatial-temporal patterns, which will help to guide meaningful CH4 mitigation practices in the livestock sector at both local and global scales.

The 2019 methane budget and uncertainties at 1° resolution and each country through Bayesian integration Of GOSAT total column methane data and a priori inventory estimates

Abstract. We use optimal estimation (OE) to quantify methane fluxes based on total column CH4 data from the Greenhouse Gases Observing Satellite (GOSAT) and the GEOS-Chem global chemistry transport model. We then project these fluxes to emissions by sector at 1∘ resolution and then to each country using a new Bayesian algorithm that accounts for prior and posterior uncertainties in the methane emissions. These estimates are intended as a pilot dataset for the global stock take in support of the Paris Agreement. However, differences between the emissions reported here and widely used bottom-up inventories should be used as a starting point for further research because of potential systematic errors of these satellite-based emissions estimates. We find that agricultural and waste emissions are ∼ 263 ± 24 Tg CH4 yr−1, anthropogenic fossil emissions are 82 ± 12 Tg CH4 yr−1, and natural wetland/aquatic emissions are 180 ± 10 Tg CH4 yr−1. These estimates are consistent with previous inversions based on GOSAT data and the GEOS-Chem model. In addition, anthropogenic fossil estimates are consistent with those reported to the United Nations Framework Convention on Climate Change (80.4 Tg CH4 yr−1 for 2019). Alternative priors can be easily tested with our new Bayesian approach (also known as prior swapping) to determine their impact on posterior emissions estimates. We use this approach by swapping to priors that include much larger aquatic emissions and fossil emissions (based on isotopic evidence) and find little impact on our posterior fluxes. This indicates that these alternative inventories are inconsistent with our remote sensing estimates and also that the posteriors reported here are due to the observing and flux inversion system and not uncertainties in the prior inventories. We find that total emissions for approximately 57 countries can be resolved with this observing system based on the degrees-of-freedom for signal metric (DOFS > 1.0) that can be calculated with our Bayesian flux estimation approach. Below a DOFS of 0.5, estimates for country total emissions are more weighted to our choice of prior inventories. The top five emitting countries (Brazil, China, India, Russia, USA) emit about half of the global anthropogenic budget, similar to our choice of prior emissions but with the posterior emissions shifted towards the agricultural sector and less towards fossil emissions, consistent with our global posterior results. Our results suggest remote-sensing-based estimates of methane emissions can be substantially different (although within uncertainty) than bottom-up inventories, isotopic evidence, or estimates based on sparse in situ data, indicating a need for further studies reconciling these different approaches for quantifying the methane budget. Higher-resolution fluxes calculated from upcoming satellite or aircraft data such as the Tropospheric Monitoring Instrument (TROPOMI) and those in formulation such as the Copernicus CO2M, MethaneSat, or Carbon Mapper can be incorporated into our Bayesian estimation framework for the purpose of reducing uncertainty and improving the spatial resolution and sectoral attribution of subsequent methane emissions estimates.

High methane emissions from an anoxic fjord driven by mixing and oxygenation

AbstractOceanic methane (CH4) budgets lack data from high‐latitude fjords that often behave as intermittently anoxic ecosystems with potentially high methane emissions. We conducted 15 expeditions and 49 in situ lander deployments in an anoxic Scandinavian fjord between 2009 and 2021. Benthic fluxes were highest at the deepest anoxic site (average 516 μmol CH4 m−2 d−1), supporting bottom water methane exceeding 5000 nM. Natural and engineered mixing events displaced methane‐rich bottom waters, enhancing upper water concentrations and driving high sea–air flux reaching 641 μmol CH4 m−2 d−1. Mixing also reduced pelagic methane oxidation from 70% to 20% of all methane sources into the fjord. Upscaling of literature fluxes combined with our results suggests that fjords globally emit 1.0 ± 0.8 Tg CH4 yr−1. Despite their small global area, fjords are hotspots of methane release. We suggest that ongoing deoxygenation and global change will enhance methane emissions from fjords.

Continuous CH<sub>4</sub> and <i>δ</i><sup>13</sup>CH<sub>4</sub> measurements in London demonstrate under-reported natural gas leakage

Abstract. Top-down greenhouse gas measurements can be used to independently assess the accuracy of bottom-up emission estimates. We report atmospheric methane (CH4) mole fractions and δ13CH4 measurements from Imperial College London from early 2018 onwards using a Picarro G2201-i analyser. Measurements from March 2018 to October 2020 were compared to simulations of CH4 mole fractions and δ13CH4 produced using the NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model coupled with the UK National Atmospheric Emissions Inventory, UK NAEI, and a global inventory, the Emissions Database for Global Atmospheric Research (EDGAR), with model spatial resolutions of ∼ 2, ∼ 10, and ∼ 25 km. Simulation–measurement comparisons are used to evaluate London emissions and the source apportionment in the global (EDGAR) and UK national (NAEI) emission inventories. Observed mole fractions were underestimated by 30 %–35 % in the NAEI simulations. In contrast, a good correspondence between observations and EDGAR simulations was seen. There was no correlation between the measured and simulated δ13CH4 values for either NAEI or EDGAR, however, suggesting the inventories' sectoral attributions are incorrect. On average, natural gas sources accounted for 20 %–28 % of the above background CH4 in the NAEI simulations and only 6 %–9 % in the EDGAR simulations. In contrast, nearly 84 % of isotopic source values calculated by Keeling plot analysis (using measurement data from the afternoon) of individual pollution events were higher than −45 ‰, suggesting the primary CH4 sources in London are actually natural gas leaks. The simulation–observation comparison of CH4 mole fractions suggests that total emissions in London are much higher than the NAEI estimate (0.04 Tg CH4 yr−1) but close to, or slightly lower than, the EDGAR estimate (0.10 Tg CH4 yr−1). However, the simulation–observation comparison of δ13CH4 and the Keeling plot results indicate that emissions due to natural gas leaks in London are being underestimated in both the UK NAEI and EDGAR.

Model-based evaluation of methane emissions from paddy fields in East Asia

Evaluating regional budgets of methane (CH4), a potent greenhouse gas and short-lived climate forcer, is an important task for future climate management. This study estimated historical CH4 emissions from paddy fields in East Asia by using a process-based terrestrial biogeochemical model driven by climate and land-use data. To capture the range of estimation uncertainty, this study used two CH4 emission schemes, four paddy field maps, and two seasonal inundation methods for a total of 16 simulations. The mean CH4 emission rate during 2000-2015 was estimated to be 5.7 Tg CH4 yr-1, which is similar to statistical inventories and other estimates. However, the large standard deviation (± 3.2 Tg CH4 yr-1) among the simulations implies that serious estimation uncertainties remain. Three factors - CH4 emission scheme, paddy field map, and inundation seasonality - were responsible for the disparity of the estimates. Because of the lack of historical management data, the model simulation did not show a decreasing trend in the agricultural CH4 emissions. A sensitivity analysis for temperature indicated that a 1-2 °C temperature rise (typical warming in mitigation-oriented scenarios) would substantially enhance CH4 emissions. However, a sensitivity analysis for water management indicated that a lower water-table depth would largely mitigate the emission increase. Additional studies to improve agricultural datasets and models for better paddy field management are still needed.

Non-flooded riparian Amazon trees are a regionally significant methane source.

Inundation-adapted trees were recently established as the dominant egress pathway for soil-produced methane (CH4) in forested wetlands. This raises the possibility that CH4 produced deep within the soil column can vent to the atmosphere via tree roots even when the water table (WT) is below the surface. If correct, this would challenge modelling efforts where inundation often defines the spatial extent of ecosystem CH4 production and emission. Here, we examine CH4 exchange on tree, soil and aquatic surfaces in forest experiencing a dynamic WT at three floodplain locations spanning the Amazon basin at four hydrologically distinct times from April 2017 to January 2018. Tree stem emissions were orders of magnitude larger than from soil or aquatic surface emissions and exhibited a strong relationship to WT depth below the surface (less than 0). We estimate that Amazon riparian floodplain margins with a WT < 0 contribute 2.2–3.6 Tg CH4 yr−1 to the atmosphere in addition to inundated tree emissions of approximately 12.7–21.1 Tg CH4 yr−1. Applying our approach to all tropical wetland broad-leaf trees yields an estimated non-flooded floodplain tree flux of 6.4 Tg CH4 yr−1 which, at 17% of the flooded tropical tree flux of approximately 37.1 Tg CH4 yr−1, demonstrates the importance of these ecosystems in extending the effective CH4 emitting area beyond flooded lands.This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Characterizing Methane Emission Hotspots From Thawing Permafrost

AbstractMethane (CH4) emissions from climate‐sensitive ecosystems within the northern permafrost region represent a potentially large but highly uncertain source, with current estimates spanning a factor of seven (11–75 Tg CH4 yr−1). Accelerating permafrost thaw threatens significant increases in pan‐Arctic CH4 emissions, amplifying the permafrost carbon feedback. We used airborne imaging spectroscopy with meter‐scale spatial resolution and broad coverage to identify a previously undiscovered CH4 emission hotspot adjacent to a thermokarst lake in interior Alaska. Hotspot emissions were confined to &lt;1% of the 10 ha lake study area. Ground‐based chamber measurements confirmed average daily fluxes from the hotspot of 1,170 mg CH4 m−2 d−1, with extreme daily maxima up to 24,200 mg CH4 m−2 d−1. Ground‐based geophysical measurements revealed thawed permafrost directly beneath the CH4 hotspot, extending to a depth of ∼15 m, indicating that the intense CH4 emissions likely originated from recently thawed permafrost. Hotspot emissions accounted for ∼40% of total diffusive CH4 emissions from the lake study site. Combining study site findings with hotspot statistics from our 70,000 km2 airborne survey across Alaska and northwestern Canada, we estimate that pan‐Arctic terrestrial thermokarst hotspots currently emit 1.1 (0.1–5.2) Tg CH4 yr−1, or roughly 4% of the annual pan‐Arctic wetland budget from just 0.01% of the northern permafrost land area. Our results suggest that significant proportions of pan‐Arctic CH4 emissions originate from disproportionately small areas of previously undetermined thermokarst emissions hotspots, and that pan‐Arctic CH4 emissions may increase non‐linearly as thermokarst processes increase under a warming climate.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000–2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top‐down (TD) (atmospheric and transport model‐based) and bottom‐up (BU) (inventory‐ and process model‐based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions—China, Southeast Asia, USA, South Asia, and Brazil—account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4 yr−1 in 2008–2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

WETMETH 1.0: a new wetland methane model for implementation in Earth system models

Abstract. Wetlands are the single largest natural source of methane (CH4), a powerful greenhouse gas affecting the global climate. In turn, wetland CH4 emissions are sensitive to changes in climate conditions such as temperature and precipitation shifts. However, biogeochemical processes regulating wetland CH4 emissions (namely microbial production and oxidation of CH4) are not routinely included in fully coupled Earth system models that simulate feedbacks between the physical climate, the carbon cycle, and other biogeochemical cycles. This paper introduces a process-based wetland CH4 model (WETMETH) developed for implementation in Earth system models and currently embedded in an Earth system model of intermediate complexity. Here, we (i) describe the wetland CH4 model, (ii) evaluate the model performance against available datasets and estimates from the literature, and (iii) analyze the model sensitivity to perturbations of poorly constrained parameters. Historical simulations show that WETMETH is capable of reproducing mean annual emissions consistent with present-day estimates across spatial scales. For the 2008–2017 decade, the model simulates global mean wetland emissions of 158.6 Tg CH4 yr−1, of which 33.1 Tg CH4 yr−1 is from wetlands north of 45∘ N. WETMETH is highly sensitive to parameters for the microbial oxidation of CH4, which is the least constrained process in the literature.

Large Methane Emissions From the Pantanal During Rising Water‐Levels Revealed by Regularly Measured Lower Troposphere CH4 Profiles

AbstractThe Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer‐free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer‐term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks.