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China is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH4/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate. Plain Language Summary This study examines methane emissions from coal mines in Shanxi, China, identified as a significant source of global methane, a potent greenhouse gas. Using satellite data over 2 years, we found 138 detected episodic events at 82 facilities in Shanxi emitting an estimated 1.2 million tons of methane annually, far exceeding emissions from known global hotspots in the oil and gas sectors. Our analysis shows that a small percentage of these facilities contribute to the majority of emissions. The results, which challenge previous estimates from environmental monitoring organizations, underscore the need for direct, observational methods to accurately assess and address methane emissions. This work aims to guide efforts in mitigating climate change by identifying key areas for reducing methane release. Key Points Satellite data identify 82 major methane emitters in Shanxi, China, with high annual emissions of up to 1.2 Mt Top 20% of coal mines contribute to half of the region's total methane emissions Findings highlight the importance of direct measurements for accurate emission estimates

Agriculture produces greenhouse gases. Methane is a result of manure degradation and microbial fermentation in the rumen. Reduced CH4 emissions will slow climate change and reduce greenhouse gas concentrations. This review compiled studies to evaluate the best ways to decrease methane emissions. Longer rumination times reduce methane emissions and milk methane. Other studies have not found this. Increasing propionate and reducing acetate and butyrate in the rumen can reduce hydrogen equivalents that would otherwise be transferred to methanogenesis. Diet can reduce methane emissions. Grain lowers rumen pH, increases propionate production, and decreases CH4 yield. Methane generation per unit of energy-corrected milk yield reduces with a higher-energy diet. Bioactive bromoform discovered in the red seaweed Asparagopsis taxiformis reduces livestock intestinal methane output by inhibiting its production. Essential oils, tannins, saponins, and flavonoids are anti-methanogenic. While it is true that plant extracts can assist in reducing methane emissions, it is crucial to remember to source and produce plants in a sustainable manner. Minimal lipid supplementation can reduce methane output by 20%, increasing energy density and animal productivity. Selecting low- CH4 cows may lower GHG emissions. These findings can lead to additional research to completely understand the impacts of methanogenesis suppression on rumen fermentation and post-absorptive metabolism, which could improve animal productivity and efficiency.

Recent studies have confirmed significant tree-mediated methane emissions in wetlands; however, conditions and processes controlling such emissions are unclear. Here we identify factors that control the emission of methane from Alnus glutinosa. Methane fluxes from the soil surface, tree stem surfaces, leaf surfaces and whole mesocosms, pore water methane concentrations and physiological factors (assimilation rate, stomatal conductance and transpiration) were measured from 4-yr old A. glutinosa trees grown under two artificially controlled water-table positions. Up to 64% of methane emitted from the high water-table mesocosms was transported to the atmosphere through A. glutinosa. Stem emissions from 2 to 22 cm above the soil surface accounted for up to 42% of total tree-mediated methane emissions. Methane emissions were not detected from leaves and no relationship existed between leaf surface area and rates of tree-mediated methane emissions. Tree stem methane flux strength was controlled by the amount of methane dissolved in pore water and the density of stem lenticels. Our data show that stem surfaces dominate methane egress from A. glutinosa, suggesting that leaf area index is not a suitable approach for scaling tree-mediated methane emissions from all types of forested wetland.

Wetlands are a significant natural source of methane (CH4), and their emissions are highly sensitive to climatic variability. This study investigated the impact of El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on wetland CH4 emission anomalies based on a model simulation data set from 1980 to 2022. Results showed El Niño reduced CH4 emission anomalies, whereas La Niña increased them. Both ENSO and IOD phases greatly altered climatic variables, which in turn affected CH4 emission anomalies. The global mean sensitivities of CH4 emission anomalies to ENSO and IOD were −0.17 and −0.08 mg CH4 m−2 month−1, respectively, which exhibited pronounced temporal and spatial variability, mainly driven by soil moisture and temperature. This study underscored the pronounced temporal and spatial variability of CH4 emission anomalies under ENSO and IOD phases, which would improve the accuracy of predictive CH4 models and inform strategies for mitigating climate change. Plain Language Summary This study investigated the influence of the El Niño‐Southern Oscillation (ENSO), with El Niño and La Niña phases, and the Indian Ocean Dipole (IOD), with its positive and negative phases, on wetland methane (CH4) emission anomalies based on a model simulation data set from 1980 to 2022. Results showed that El Niño reduced CH4 emission anomalies, whereas La Niña increased them. Similarly, positive IOD (pIOD) and negative IOD (nIOD) also modulated CH4 emission anomalies. Compound events, such as El Niño with pIOD or nIOD, greatly decreased CH4 emission anomalies, particularly in summer and autumn. ENSO and IOD induced notable variations in key climatic factors, including air temperature (TA), vapor pressure deficit, soil moisture (SM), and standardized precipitation‐evapotranspiration index, all of which correlated with CH4 emission anomalies. The global averaged sensitivities of CH4 emission anomalies to ENSO and IOD were −0.17 and −0.08 Tg CH4 month−1, respectively, which were mainly dominated by TA and SM. This study underscored the critical role of climatic variables in shaping the CH4 emission anomalies in response to ENSO and IOD phases, highlighting the complex interactions between ENSO, IOD, and wetland CH4 dynamics at a global scale. Key Points La Niña stimulated but EI Niño inhibited CH4 emission anomalies Global CH4 emission anomalies were negatively affected by the El Niño‐Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) indices Soil moisture and air temperature dominated CH4 emission anomalies during the ENSO and IOD periods

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH4 yr−1 for Russia, 12.8 to 14.3 Tg CH4 yr−1 for temperate South America, and 43.2 to 44.9 Tg CH4 yr−1 for contiguous USA, and the values decrease from 54 to 51.3 Tg CH4 yr−1 for China, 26.2 to 25.5 Tg CH4 yr−1 for Europe, and by 12.4 Tg CH4 yr−1 for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC.

We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a−1 more anthropogenic emissions for China and around 4 Tg a−1 more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH4 data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH4 data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results.

Rice agriculture is a major source of atmospheric methane, but current emission inventories are highly uncertain, mostly due to poor rice‐specific inundation data. Inversions of atmospheric methane observations can help to better quantify rice emissions but require high‐resolution prior information on the location and timing of emissions. Here we use Landsat satellite data at 30 m resolution to map the global monthly distribution of rice paddy fractional areas on a 0.1° × 0.1° (∼10 × 10 km) grid by optimizing an algorithm for flooded vegetation and combining it with a 30 m global cropland database and rice‐specific data. We validate this global rice paddy map with an independent US rice database and with seasonal flux measurements from the FLUXNET CH4 network, estimating errors on rice area fraction of 31% on the 0.1° × 0.1° grid and 10% regionally. We combine the rice paddy map with an extensive global data set of emission factors (EFs) per unit of rice paddy area. The resulting Global Rice Paddy Inventory (GRPI) provides methane emission estimates at 0.1° × 0.1° (∼10 × 10 km) spatial resolution and monthly resolution. Our global emission of 39.3 ± 4.7 Tg a−1 for 2022 (best estimate and error standard deviation) is higher than previous inventories that use outdated rice maps and IPCC‐recommended EFs now considered to be too low. China is the largest rice emitter in GRPI (8.2 ± 1.0 Tg a−1), followed by India (6.5 ± 1.0 Tg a−1), Bangladesh (5.7 ± 1.2 Tg a−1), Vietnam (5.7 ± 1.0 Tg a−1), and Thailand (4.4 ± 0.9 Tg a−1). These five countries together account for 78% of global total rice emissions. Seasonality of emissions varies considerably between and within individual countries reflecting differences in climate and crop practices. We define a rice methane intensity (methane emission per unit of rice produced) to assess the potential of mitigating methane emission without compromising food security. We find national methane intensities ranging from 10 to 120 kg methane per ton of rice produced (global mean 51) for major rice‐growing countries. Countries can achieve low intensities with high‐yield cultivars, upland rice agriculture, water management, and organic matter management. Plain Language Summary Rice agriculture is a major source of atmospheric methane, a potent greenhouse gas with strong warming potential. Current emission estimates for rice agriculture are highly uncertain because of poor inundation data. Here we use Landsat satellite data to develop a new Global Rice Paddy Inventory (GRPI) of methane emissions at 10 km resolution for each month of 2022. We find that global rice methane emissions are higher than previously thought, at 39.3 million metric tons in 2022. Five countries (China, India, Bangladesh, Vietnam, and Thailand) account for 78% of these emissions. We introduce a metric of methane intensity ‐ methane emitted per ton of rice produced ‐ to assess the potential to reduce methane emission without compromising food security. We find that methane intensities vary widely between countries. Key Points We developed a new Global Rice Paddy Inventory of methane emissions at 0.1° × 0.1° monthly resolution using Landsat satellite inundation data Our global emission of 39.3 ± 4.7 Tg a−1 is higher than previous inventories that use outdated rice maps and IPCC‐recommended emission factors now considered too low Countries can mitigate methane without compromising food security by developing high‐yield cultivars, upland rice agriculture, water management, and organic matter management

The venting of methane from coal mining is China’s main source of methane emissions. Accurate and up-to-date methane emission factors for coal mines are significant for reporting and controlling methane emissions in China. This study takes a typical coal mine in Shanxi Province as the research object and divides the coal mine into different zones based on the occurrence structure of methane in Shanxi Province. The methane emission characteristics of underground coal mine types and monitoring modes were studied. The emissions of methane from coal seams and ventilation methane of six typical coal mine groups in Shanxi Province were monitored. The measured methane concentration data were corrected by substituting them into the methane emission formula, and the future methane emissions were predicted by the coal production and methane emission factors. The results show that the number of methane mines and predicted reserves in Zone I of Shanxi Province are the highest. The average methane concentration emitted from coal and gas outburst mines is about 22.52%, and the average methane concentration emitted from high-gas mines is about 10.68%. The methane emissions from coal and gas outburst mines to the atmosphere account for about 64% of the total net methane emissions. The predicted methane emission factor for Shanxi coal mines is expected to increase from 8.859 m3/t in 2016 to 9.136 m3/t in 2025, and the methane emissions from Shanxi coal mines will reach 8.43 Tg in 2025.

Methane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET‐ CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance‐based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET‐CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques. Plain Language Summary Methane is a critical greenhouse gas, and wetlands are the largest natural source of it. Accurately predicting methane emissions from wetlands is key to tackling climate change. But these predictions, made through computer models, are seldom spot‐on. Why? Because there are many factors in the models that lead to uncertain predictions. A major source of this uncertainty arises from the empirical model parameters. Just as tuning a radio dial ensures clear reception, models need properly adjusted parameters for accurate predictions. A sensitivity analysis was performed to determine which parameters are most crucial for accurate predictions. Instead of running the complex numerical model every time, machine learning was employed to create a faster and simpler version. Using this approach, five parameters were pinpointed as particularly sensitive, significantly impacting the predictions. The comparison of model‐predicted methane emissions with real‐world measurements showed that the model performed well in some cases but needed tweaking in others. Refining these sensitive parameters with more real‐world observations could make better predictions in the future. Key Points Identified five key sensitive parameters for methane emissions using the Sobol sensitivity analysis method Parameters linked to production and diffusion present the highest sensitivities despite apparent seasonal variation Fourteen out of nineteen model parameters exert negligible influence on methane emissions

The large uncertainties in estimating CH4 emissions from wetland ecosystems, the leading natural source to the atmosphere, substantially hinder the quantification of the global CH4 budget. This study used the IBIS‐CH4 (Integrated BIosphere Simulator‐Methane) model, a process‐based model integrating microbial mechanisms associated with CH4 production and oxidation processes, to simulate global wetland CH4 emissions from 2001 to 2020. Initially, we employed the IBIS‐CH4 model to evaluate its performance across 26 diverse wetland sites worldwide. The results showed that the magnitude and seasonality of observed CH4 fluxes over various wetland sites were well reproduced. We then used this model to estimate the annual global wetland CH4 emissions from 2001 to 2020, averaging 152.67 Tg CH4 yr−1, with a range of 135.72–167.57 Tg CH4 yr−1. The estimated global wetland CH4 emissions are generally in agreement with the current bottom‐up estimates (117–256 Tg CH4 yr−1) and closely overlap with independent top‐down estimates (139–183 Tg CH4 yr−1). During 2001–2020, the estimated global wetland CH4 emissions initially showed an increasing trend, followed by a decline. The peak of CH4 emissions reached in 2010, coinciding with the peak of wetland area. The majority of global wetland CH4 emissions were concentrated in tropical regions, which exhibited a clear seasonality and had a peak in July. The impact of meteorological factors on wetland CH4 emissions was greater than that of leaf area index, indicating the importance of soil hydrothermal conditions on wetland CH4 emissions. Plain Language Summary Wetlands are crucial sources of methane (CH4), an important greenhouse gas, while accurately estimating wetland CH4 emissions has been challenging. This research simulated global wetland CH4 emissions from 2001 to 2020 using a process‐based model known as IBIS‐CH4. The model was tested at 26 wetland sites globally and accurately predicted observed CH4 emissions. Overall, the study estimated that global wetlands emitted about 152.67 Tg CH4 yr−1 annually, which agrees with alternative estimates. The estimated wetland CH4 emissions exhibited a tendency of initially rising and then decreasing, reaching a peak in 2010. Most CH4 emissions came from tropical regions and showed a clear seasonality. Additionally, the study revealed that soil hydrothermal conditions and wetland area were key factors influencing CH4 emissions from wetlands, emphasizing the relevance of climate change in controlling CH4 emissions. Key Points We used a process‐based model to simulate global wetland CH4 emissions during 2001–2020, averaging 152.67 Tg CH4 yr−1 Global wetland CH4 emissions increased at first and then decreased, peaking in 2010, consistent with the peak of wetland areas Based on the model experiments, soil hydrothermal conditions and wetland area were the main factors controlling wetland CH4 emissions