Surface Methane Research Articles

Methane storm characteristics and evolution in simulations of Titan’s hydroclimate

Methane precipitation is a key component of the climate on Titan, and has been shown to impact surface features. Recent general circulation models (GCMs) have reproduced Titan’s hydroclimate, including precipitation, with increasing accuracy, yet characterization of their simulated precipitation events is lacking. We investigate the characteristics and evolution of methane storms simulated over 40 Titan years using the Titan Atmospheric Model, a validated GCM. Storms are identified and tracked using the density-based spatial clustering of applications with noise (DBSCAN) algorithm, allowing them to be followed through time and space. We find that storms follow seasonality expected from observations and prior modeling, occur preferentially in the summer hemisphere, and tend to start over high topography. The population of storms is bimodal in traits corresponding to intensity, area, and duration, with a large population of small, short-lived, and weakly precipitating storms and a smaller population of exceptionally large, long-lasting, and intense storms. These largest storms tend to evolve similarly over their lifetimes, peaking early in intensity and in the middle of their lives in area. We also find temporal clustering of storms, in alignment with observations and the proposed relaxation-oscillation model of Titan’s methane precipitation. These storm clusters emerge quasi-periodically following long dry spells during which evaporation of surface methane recharges atmospheric moisture. Approximately five clusters occur per Titan year, and their locations are strongly seasonal. Overall, our quantitative descriptions of storms and storm clusters over a long timescale provide additional insight into Titan’s methane cycle and surface features, and may assist in the planning of future missions such as Dragonfly.

Global atmospheric methane uptake by upland tree woody surfaces

Methane is an important greenhouse gas1, but the role of trees in the methane budget remains uncertain2. Although it has been shown that wetland and some upland trees can emit soil-derived methane at the stem base3,4, it has also been suggested that upland trees can serve as a net sink for atmospheric methane5,6. Here we examine in situ woody surface methane exchange of upland tropical, temperate and boreal forest trees. We find that methane uptake on woody surfaces, in particular at and above about 2 m above the forest floor, can dominate the net ecosystem contribution of trees, resulting in a net tree methane sink. Stable carbon isotope measurement of methane in woody surface chamber air and process-level investigations on extracted wood cores are consistent with methanotrophy, suggesting a microbially mediated drawdown of methane on and in tree woody surfaces and tissues. By applying terrestrial laser scanning-derived allometry to quantify global forest tree woody surface area, a preliminary first estimate suggests that trees may contribute 24.6–49.9 Tg of atmospheric methane uptake globally. Our findings indicate that the climate benefits of tropical and temperate forest protection and reforestation may be greater than previously assumed.

Estimation of Surface Methane Concentration based on the Ensemble Kalman Filter Algorithm using a Transport Chemical Model

Estimation of Surface Methane Concentration based on the Ensemble Kalman Filter Algorithm using a Transport Chemical Model

Synthesis Product for Ocean Time Series (SPOTS) – a ship-based biogeochemical pilot

Abstract. The presented pilot for the Synthesis Product for Ocean Time Series (SPOTS) includes data from 12 fixed ship-based time-series programs. The related stations represent unique open-ocean and coastal marine environments within the Atlantic Ocean, Pacific Ocean, Mediterranean Sea, Nordic Seas, and Caribbean Sea. The focus of the pilot has been placed on biogeochemical essential ocean variables: dissolved oxygen, dissolved inorganic nutrients, inorganic carbon (pH, total alkalinity, dissolved inorganic carbon, and partial pressure of CO2), particulate matter, and dissolved organic carbon. The time series used include a variety of temporal resolutions (monthly, seasonal, or irregular), time ranges (10–36 years), and bottom depths (80–6000 m), with the oldest samples dating back to 1983 and the most recent one corresponding to 2021. Besides having been harmonized into the same format (semantics, ancillary data, units), the data were subjected to a qualitative assessment in which the applied methods were evaluated and categorized. The most recently applied methods of the time-series programs usually follow the recommendations outlined by the Bermuda Time Series Workshop report (Lorenzoni and Benway, 2013), which is used as the main reference for “method recommendations by prevalent initiatives in the field”. However, measurements of dissolved oxygen and pH, in particular, still show room for improvement. Additional data quality descriptors include precision and accuracy estimates, indicators for data variability, and offsets compared to a reference and widely recognized data product for the global ocean: the GLobal Ocean Data Analysis Project (GLODAP). Generally, these descriptors indicate a high level of continuity in measurement quality within time-series programs and a good consistency with the GLODAP data product, even though robust comparisons to the latter are limited. The data are available as (i) a merged comma-separated file that is compliant with the World Ocean Circulation Experiment (WOCE) exchange format and (ii) a format dependent on user queries via the Environmental Research Division's Data Access Program (ERDDAP) server of the Global Ocean Observing System (GOOS). The pilot increases the data utility, findability, accessibility, interoperability, and reusability following the FAIR philosophy, enhancing the readiness of biogeochemical time series. It facilitates a variety of applications that benefit from the collective value of biogeochemical time-series observations and forms the basis for a sustained time-series living data product, SPOTS, complementing relevant products for the global interior ocean carbon data (GLobal Ocean Data Analysis Project), global surface ocean carbon data (Surface Ocean CO2 Atlas; SOCAT), and global interior and surface methane and nitrous oxide data (MarinE MethanE and NiTrous Oxide product). Aside from the actual data compilation, the pilot project produced suggestions for reporting metadata, implementing quality control measures, and making estimations about uncertainty. These recommendations aim to encourage the community to adopt more consistent and uniform practices for analysis and reporting and to update these practices regularly. The detailed recommendations, links to the original time-series programs, the original data, their documentation, and related efforts are available on the SPOTS website. This site also provides access to the data product (DOI: https://doi.org/10.26008/1912/bco-dmo.896862.2, Lange et al., 2024) and ancillary data.

Microbial methanogenesis in aerobic water: A key driver of surface methane enrichment in a deep reservoir

Significant amounts of the greenhouse gas methane (CH4) are released into the atmosphere worldwide via freshwater sources. The surface methane maximum (SMM), where methane is supersaturated in surface water, has been observed in aquatic systems and contributes significantly to emissions. However, little is known about the temporal and spatial variability of SMM or the mechanisms underlying its development in artificial reservoirs. Here, the community composition of methanogens as major methane producers in the water column and the mcrA gene was investigated, and the cause of surface methane supersaturation was analyzed. In accordance with the findings, elevated methane concentration of SMM in the transition zone, with an annually methane emission flux 2.47 times higher than the reservoir average on a large and deep reservoir. In the transition zone, methanogens with mcrA gene abundances ranging from 0.5 × 103–1.45 × 104 copies/L were found. Methanobacterium, Methanoseata and Methanosarcina were the three dominate methanogens, using both acetic acid and H2/CO2 pathways. In summary, this study contributes to our comprehension of CH4 fluxes and their role in the atmospheric methane budget. Moreover, it offers biological proof of methane generation, which could aid in understanding the role of microbial methanogenesis in aerobic water.

Sub‐Diurnal Methane Variations on Mars Driven by Barometric Pumping and Planetary Boundary Layer Evolution

AbstractIn recent years, the Tunable Laser Spectrometer within the Sample Analysis at Mars (TLS‐SAM) instrument on board the Mars Science Laboratory (MSL) Curiosity rover has detected methane variations in the atmosphere at Gale crater. Methane concentrations appear to fluctuate seasonally as well as sub‐diurnally, which is difficult to reconcile with an as‐yet‐unknown transport mechanism delivering the gas from underground to the atmosphere. To potentially explain the fluctuations, we consider barometrically induced transport of methane from an underground source to the surface, modulated by temperature‐dependent adsorption. The subsurface fractured‐rock seepage model is coupled to a simplified 1‐D atmospheric mixing model to provide insights on the pattern of atmospheric methane concentrations in response to transient surface methane emissions, as well as to predict sub‐diurnal variation in methane abundance for the northern summer period, which is a candidate time frame for a MSL Curiosity sampling campaign. Our analysis suggests that there is a lower limit to the subsurface fracture density that can produce the observed methane patterns, below which the atmospheric methane variations would be out of phase with the observations. The best‐performing model scenarios indicate a significant, short‐lived methane pulse just prior to sunrise, the detection of which by TLS‐SAM would be a potential indicator of the contribution of barometric pumping to Mars' atmospheric methane variations.

Measurement of D/H and 13C/12C ratios in methane ice on Eris and Makemake: Evidence for internal activity

James Webb Space Telescope's NIRSpec infrared imaging spectrometer observed the outer solar system dwarf planets Eris and Makemake in reflected sunlight at wavelengths spanning 1 through 5 μm. Both objects have high albedo surfaces that are rich in methane ice, with a texture that permits long optical path lengths through the ice for solar photons. There is evidence for N2 ice absorption around 4.2 μm on Eris, though not on Makemake. No CO ice absorption is seen at 4.67 μm on either body. For the first time, absorption bands of two heavy isotopologues of methane are observed at 2.615 μm (13CH4), 4.33 μm (12CH3D), and 4.57 μm (12CH3D). These bands enable us to measure D/H ratios of (2.5 ± 0.5) × 10−4 and (2.9 ± 0.6) × 10−4, along with 13C/12C ratios of 0.012 ± 0.002 and 0.010 ± 0.003 in the surface methane ices of Eris and Makemake, respectively. The measured D/H ratios are much lower than that of presumably primordial methane in comet 67P/Churyumov-Gerasimenko, but they are similar to D/H ratios in water in many comets and larger outer solar system objects. This similarity suggests that the hydrogen atoms in methane on Eris and Makemake originated from water, indicative of geochemical processes in past or even ongoing hot environments in their deep interiors. The 13C/12C ratios are consistent with commonly observed solar system values, suggesting no substantial enrichment in 13C as could happen if the methane currently on their surfaces was the residue of a much larger inventory that had mostly been lost to space. Possible explanations include geologically recent outgassing from the interiors as well as processes that cycle the surface methane inventory to keep the uppermost surfaces refreshed.

Using Ground- and Drone-Based Surface Emission Monitoring (SEM) Data to Locate and Infer Landfill Methane Emissions

Ground- and drone-based surface emission monitoring (SEM) campaigns were performed at two municipal solid waste landfills, during the same week as mobile tracer correlation method (TCM) testing was used to measure the total methane emissions from the same landfills. The G-SEM and the D-SEM data, along with wind data, were used as input into an inverse modeling approach combined with an optimization-based methane emission estimation method (implemented in a tool called SEM2Flux). This approach involves the use of backward dispersion modeling to estimate the whole-site methane emissions from a given landfill and the identification of locations and emission rates of major leaks. SEM2Flux is designed to exploit the measured surface methane concentration concurrently with wind data and tackle two problems: (1) inferring the estimates of methane rates from individual landfills, and (2) identifying the likely locations of the main emission sources. SEM2Flux results were also compared with emission estimates obtained using TCM. In Landfill B, the average TCM-measured methane emissions was 1178 Kg/h, with a standard deviation of 271 Kg/h. In Landfill C, the average TCM-measured emission rate was 601 Kg/h, with a standard deviation of 292 Kg/h. For both landfills, the D-SEM data yielded statistically similar estimates of methane emissions as the TCM-measured emissions. On the other hand, the G-SEM data yielded comparable estimates of emissions to TCM-measured emissions only for Landfill C, where the D-SEM and G-SEM data were statistically not different. The results of this study showcase the ability of this method using surface concentrations to provide a rapid and simple estimation of fugitive methane emissions from landfills. Such an approach can also be used to assess the effectiveness of different remedial actions in reducing fugitive methane emissions from a given landfill.

Hybrid model combining LSTM with discrete wavelet transformation to predict surface methane concentration in the Arctic Island Belyy

In this paper, we propose a hybrid model that combines wavelet transformation of the raw data and an artificial neural network with long short-term memory (LSTM). This model allows researchers to increase the accuracy of time series forecasting.The model is based on data from environmental monitoring of greenhouse gases on the Belyy Island of the Yamal-Nenets Autonomous Okrug, Russia.The raw data for building the proposed model were obtained in July–August 2016. The CH4 concentration time series was decomposed using a discrete wavelet transform into four components - one approximating and three detailing. These components, along with a timed temperature series, were used to train six ANNs—two exogenous input autoregressive networks (NAR) and four LSTM networks. The forecast was calculated as the sum of forecasts for each of the components. Forecast accuracy was assessed using several indices (mean absolute error (MAE), root mean square error (RMSE), mean square relative error (RMSRE), Willmott's agreement index (IA1, IA2)) and a Taylor diagram. The hybrid model based on LSTM showed the best accuracy. The hybrid model based on LSTM showed the best accuracy. MAE, RMSE, and RMSRE errors decreased by more than 70%. In terms of IA1 and IA2, the models improved by 11% and 30% (when comparing the best hybrid models LSTM and NAR. For the hybrid LSTM model errors RMSE, RMSRE, and MAE are more than 20% less than for the base LSTM model for training in which data without wavelet transform were used. Willmott agreement indices (IA1, IA2) rose from 32% to 59%, depending on the models being compared. So, the increase in accuracy after applying the described approach was up to 79% for models based on LSTM (more precisely, from 20% to 79%, depending on the indicator). Applying wavelet transform data to train the NAR-based model reduced MAE, RMSE, and RMSRE errors by 27%, 30%, and 31%, respectively. The Willmott agreement indices (IA1, IA2) rose from 41% to 45% respectively. The Taylor diagram also shows the advantage of the proposed approach.

Hotspot Detection and Estimation of Methane Emissions from Landfill Final Cover

The main objectives of this study were to identify methane hotspots through spatial distribution tests of the surface methane concentration above a landfill final cover and to investigate the effects of rainfall, atmospheric pressure, ground temperature, and ambient methane concentration on methane emissions. A portable laser methane detector was used to measure the spatial distribution of methane concentrations. The methane concentration distribution showed a distinct spatial variability. The maximum methane concentration reached 3225 ppm, while 73.0% of the methane concentration values were below 10.0 ppm. Several meteorological factors were found to be associated with the variation in methane emissions. Rainfall limited gas transport in the cover, resulting in more significant methane hotspots. Atmospheric pressure was negatively correlated with methane emission. The ambient methane concentration and methane flux had a significant positive linear correlation. Based on a linear correlation equation, the spatial distribution of methane concentrations in the landfill could be converted into a methane emission distribution. The estimated average value for methane emissions in the test area was approximately 4.3 g m−2 d−1. This study provides an experimental basis for locating methane hotspots and assessing methane emissions in landfill final covers, and proposes supplementary means for detecting geomembrane damage in landfill covers.

Estimation of total landfill surface methane emissions using geospatial approach combined with measured surface ambient air methane concentrations

ABSTRACT The concentration of surface air methane (CH4) measured in parts per million by volume (ppmv) near the soil/atmosphere interface should, in theory, have a positive correlation with surface methane emissions fluxes, measured in grams per square meter per day (gm−2d−1). Some researchers suggest that CH4 flux can be reasonably inferred from simple measurements of CH4 concentrations near the landfill surface. Ground-based and drone-based surface emissions monitoring (SEMs) were performed at several municipal solid waste landfills as tracer correlation method (TCM) testing was being used to measure total methane emissions from the same landfills. The TCM data and SEM data were used to establish a new simple correlation to convert surface methane concentrations in ppmv to localized surface methane emission flux in gm−2d−1. The SEM data obtained from ten ground and drone monitoring campaigns were log-transformed and geospatially treated using inverse distance weighting to the power of 2 to predict methane surface concentrations in the entire footprint of the SEM measurements area. The developed new correlation equation was then used to convert every predicted surface methane concentration to an emissions flux. The total estimate of surface emissions from the entire landfill was obtained by integrating the predicted fluxes over the area of the footprint of the SEM measurement area. The use of the new developed correlation resulted in higher total emissions estimates than other correlations reported in the literature and should be considered more conservative. Not including other factors, the proposed approach provides estimate of total methane emissions with a coefficient of variation of 20%. This study introduces a novel approach that utilizes a developed correlation between surface methane concentrations and surface emissions fluxes to estimate total methane emissions from municipal solid waste landfills or from a specified area. This study provides an additional use of the quarterly SEM data. Implications: The proposed approach provides an occasion for additional use of the easily obtainable quarterly SEMs data that can be performed by most landfills. The SEMs data are the most abundant landfill methane concentrations data. This approach gives them more benefit for the user. It is intended to convert ambient air concentrations to some estimates of surface emissions that can help landfill owners with decision making such as remediation activities or adjustments of their gas collection a systems.

Effect of Surface Methane Controls on Ozone Concentration and Rice Yield in Asia

Surface methane (CH4) is a significant precursor of tropospheric ozone (O3), a greenhouse gas that detrimentally impacts crops by suppressing their physiological processes, such as photosynthesis. This relationship implies that CH4 emissions can indirectly harm crops by increasing troposphere O3 concentrations. While this topic is important, few studies have specifically examined the combined effects of CH4 and CH4-induced O3 on rice yield and production. Utilizing the GEOS-Chem model, we assessed the potential reduction in rice yield and production in Asia against a 50% reduction in anthropogenic CH4 emissions relative to the 2010 base year. Based on O3 exposure metrics, the results revealed an average relative yield loss of 9.5% and a rice production loss of 45,121 kilotons (Kt) based on AOT40. Regions such as the India-Gangetic Plain and the Yellow River basin were particularly affected. This study determined that substantial reductions in CH4 concentrations can prevent significant rice production losses. Specifically, curbing CH4 emissions in the Beijing-Tianjin-Hebei region could significantly diminish the detrimental effects of O3 on rice yields in China, Korea, and Japan. In summary, decreasing CH4 emissions is a viable strategy to mitigate O3-induced reductions in rice yield and production in Asia.

Characterisation of Waste and Assessment of Surface Methane Emissions by Static Chamber Technique at a Major Dumping Site in Central India

Given the vast amount and higher organic content of waste generated by developing nations such as India, as well as the challenges related to waste management and global warming, controlling methane emissions from such municipal solid waste (MSW) dumpsites becomes a major concern. As a result, studying the characteristics of solid waste dumped and the subsequent emissions of methane (CH4) from a site lacking proper disposal and gas emission management facilities, as is common in developing countries, becomes more important for suggesting appropriate corrective measures. In this study, MSW samples were collected from the Bhandewadi dumping site, a prominent site in Nagpur city and subjected to proximate, ultimate, and biochemical analysis. The results showed that the waste had high moisture content due to the tropical climate of the region which, together with the greater carbon content and organic matter (OM), may be responsible for increased overall greenhouse gas emissions. Biochemical study, on the other hand, revealed lower lignin content when compared with cellulose and hemicellulose, which are key contributors to CH4 emissions. The actual on site measurements using static chamber technique at fresh dumping sites showed that the methane (CH4) flux was between 1 and 14.3 mg m-2 sec-2 and 0.9 to 7.11 g m-3 day-2 at old dumping areas. The study contributes to a better understanding of the amount and unpredictability of methane produced by solid waste in an unmanaged dumping site.

Influence of Surface Methane on Tropospheric Ozone Concentrations and Cereal Yield in Asia

Methane (CH4) emanating from terrestrial sources serves as a precursor for the genesis of tropospheric ozone (O3), a pernicious atmospheric contaminant that adversely modulates the physiological mechanisms of agricultural crops. Despite the acknowledged role of CH4 in amplifying O3 concentrations, the extant literature offers limited quantitative evaluations concerning the repercussions of CH4-mediated O3 on cereal yields. Employing the GEOS-Chem atmospheric chemistry model, the present investigation elucidates the ramifications of a 50% diminution in anthropogenic CH4 concentrations on the yield losses of maize, soybean, and wheat across Asia for the fiscal year 2010. The findings unveil pronounced yield detriments attributable to O3-induced phytotoxicity, with the Indo-Gangetic Plain and the North China Plain manifesting the most substantial yield impairments among the crops examined. A halving of anthropogenic CH4 effluents could ameliorate considerable losses in cereal production across these agriculturally pivotal regions. CH4-facilitated O3 exerts a pernicious influence on cereal yields; nevertheless, targeted mitigation of CH4 effluents, particularly in the vicinity of the North China Plain, holds the potential to substantially attenuate O3 contamination, thereby catalyzing an enhancement in regional cereal production.

A Field-Verified Model to Estimate Landfill Methane Flux Using Surface Methane Concentration Measurements under Calm Wind Conditions

A significant portion of anthropogenic greenhouse gas emissions––that are suspected of contributing to global warming––are emitted from landfills accepting biodegradable organic waste. Thus, the assessment of fugitive methane emissions from landfill surfaces has become an important and integral part of determining the overall carbon budgets of individual countries. Quantifying fugitive methane emissions from landfills using indirect methods, such as predictive mathematical models, which has been the norm for a long time, have proven to be ineffective for current needs. In recent years, attention has shifted to direct measurements, such as the flux chamber method, which is also costly and labor intensive, considering the vast areas covered by landfill surfaces. However, collecting surface methane concentration (SMC) data through an instantaneous emission measurement technique is relatively simple and inexpensive. Although the SMC is only a qualitative indicator of the methane flux of a landfill, recent literature on this topic has pointed to a potentially strong correlation between SMCs and methane flux. In this context, establishing a simple but robust model, capable of estimating methane surface flux using SMC values, was the primary aim of the research described here. In this study, we investigated the correlation between SMCs and methane flux across the soil–atmosphere boundary in a small-scale test cell under a partially controlled environment. The data collected from the test cell were used to propose a linear regression model that suggested that the methane flux under calm wind conditions could be simply predicted as 1.264 times the SMC. This model was then verified in a field lysimeter, which consisted of material similar to what is found in a landfill biocover. The model was able to predict the methane flux at the field lysimeter with an absolute average error of 40%. This error margin is quite reasonable because, as per the literature, error margins as high as 190% are not uncommon in estimating the methane flux at landfills using other commonly adopted methods.

Microbial activity contributes to spatial heterogeneity of wetland methane fluxes.

The emission of methane from wetlands is spatially heterogeneous, as concurrently measured surface fluxes can vary by orders of magnitude within the span of a few meters. Despite extensive study and the climatic significance of these emissions, it remains unclear what drives large, within-site variations. While geophysical factors (e.g., soil temperature) are known to correlate with methane (CH4) flux, measurable variance in these parameters often declines as spatial and temporal scales become finer. As methane emitted from wetlands is the direct, net product of microbial metabolisms which both produce and degrade CH4, it stands to reason that characterizing the spatial variability of microbial communities within a wetland-both horizontally and vertically-may help explain observed variances in flux. To that end, we surveyed microbial communities to a depth of 1 m across an ombrotrophic peat bog in Maine, USA using amplicon sequencing and gene expression techniques. Surface methane fluxes and geophysical factors were concurrently measured. Across the first meter of peat at the site, we observed significant changes in the abundance and composition of methanogenic taxa at every depth sampled, with variance in methanogen abundance explaining 70% of flux heterogeneity at a subset of plots. Among measured environmental factors, only peat depth emerged as correlated with flux, and had significant impact on the abundance and composition of methane-cycling communities. These conclusions suggest that a heightened awareness of how microbial communities are structured and spatially distributed within wetlands could offer improved insights into predicting CH4 flux dynamics. IMPORTANCE Globally, wetlands are one of the largest sources of methane (CH4), a greenhouse gas with a warming impact significantly greater than CO2. Methane produced in wetlands is the byproduct of a group of microorganisms which convert organic carbon into CH4. Despite our knowledge of how this process works, it is still unclear what drives dramatic, localized (<10 m) variance in emission rates from the surface of wetlands. While environmental conditions, like soil temperature or water table depth, correlate with methane flux when variance in these factors is large (e.g., spring vs fall), the explanatory power of these variables decline when spatial and temporal scales become smaller. As methane fluxes are the direct product of microbial activity, we profiled how the microbial community varied, both horizontally and vertically, across a peat bog in Maine, USA, finding that variance in microbial communities was likely contributing to much of the observed variance in flux.

Модель для прогнозирования поверхностной концентрации метана в арк­тическом регионе, основанная на искусственной нейронной сети с длинной цепью элементов краткосрочной памяти и вейвлет-преобразованием исходных данных

The study of the dynamics of greenhouse gases in the Arctic regions of the planet is becoming increasingly important. Such studies are especially relevant due to the climate change observed in this region. The paper propose a hybrid model that combines wavelet transformation of the original data and an artificial neural network with a long chain of short-term memory (LSTM) elements to predict changes in the surface methane concentration in the Arctic latitudes. The methane concentration time series via a discrete wavelet transform was decomposed into four components — one approximating and three detailing ones. These components were used to train the LSTM network. The forecast was calculated as the sum of forecasts for each of the components. Three predictive models were built. In the first, the LSTM network was trained in a non-linear autoregressive mode. The second one was a combination of discrete wavelet transform with LSTM neural network. An additional model based on a non-linear autoregressive neural network (NAR) was also used for comparison. The work is based on data from environmental monitoring of greenhouse gases on Bely Island, Yamalo-Nenets Autonomous Area of Russia. The initial data for building the proposed model were obtained within July-August 2017. The accuracy of the forecast was assessed using several indicators. The hybrid model based on LSTM showed the best accuracy.

Study of Methane Emission and Geological Sources in Northeast China Permafrost Area Related to Engineering Construction and Climate Disturbance Based on Ground Monitoring and AIRS

China’s largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing’an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China.

An intrusive cryomagmatic origin for northern radial labyrinth terrains on Titan and implications for the presence of crustal clathrates

Titan's labyrinth terrains are an organic-rich, topographically elevated, highly dissected and puzzling geomorphic unit. How these features came to be composed of organics and remain elevated may hold clues about Titan's complicated history, and in particular the dynamics and composition of Titan's crust. One subtype of labyrinth terrains, the radially networked labyrinth terrains, is found in Titan's mid-latitudes. They are dome-shaped with radial drainage patterns and appear to be a clustering of uplifted, organic-rich dissected plateaux. We use scaling relationships to determine whether they formed as elevated surfaces that were uplifted by solid-state diapirs or cryomagmatic laccoliths at depth. Based on the large, variable spacing between features, we find it unlikely that they formed via density-driven diapirism. Instead, their dimensions suggest that they are cryomagmatic intrusions that formed near the most prominent rheological contrast in the ice shell, the brittle–ductile transition (BDT). At that location, we argue that intrusions spread horizontally and inflate, forming large cryomagmatic laccoliths (upturned, large saucer-shaped sills). The intrusions would flex the overlying lithosphere and surficial sedimentary layers (likely undifferentiated plains), resulting in prominent domed features that are susceptible to erosion and incision by methane rain and wind. This process then leads to the highly dissected, dome-shaped labyrinth terrains. To determine whether the laccoliths formed near Titan's BDT, we calculate lithospheric strength envelopes for a pure water ice shell with and without an insulating methane clathrate crust using two conductive heat flows: 4 and 7 mW/m2. The plausible range for the BDT for an ice shell with a 1 km thick methane clathrate crust is 12–34 km for the 4 mW/m2 heat flow. This agrees well with the expected intrusion depths of the laccoliths (21–28 km) associated with a cluster of radial labyrinths in Titan's northern mid-latitudes, as derived from a scaling relationship relating intrusion depth to dome width. We find that methane clathrate thicknesses of 2 km or greater result in a BDT that is generally too shallow (3–24 km) to match our observations. If we consider the higher heat flow, these BDTs are even shallower. Although methane clathrate is known to be stronger than ice, its low thermal conductivity significantly raises the underlying ice shell's temperature, which results in a significantly thinner lithosphere that is not able to support these large plateaux. We conclude that in the location of this radial labyrinth terrain cluster there may be intrusive cryomagmatic activity approximately 21 km deep within the water ice shell, with a putative surface methane clathrate crust, if it exists atop a conductive ice shell, that is constrained to be <2 km thick.

Analysis of spatiotemporal distribution of methane column concentration and emission source in permafrost area of Northeast China based on AIRS data

Northeast China has the largest high-latitude permafrost distribution zone in China. With the intensification of global warming, the carbon stored in the permafrost will gradually thaw and release in the form of methane gas to air, thus increasing the methane column concentration in the near-surface troposphere. However, at present, the spatiotemporal distribution and emission source of methane column concentration in the near-surface troposphere are not clear. In this paper, using the AIRS (Atmospheric Infrared Sounder) remote sensing data from Aqua satellite, we analyzed the distribution and change trend of the methane column concentration in the near-surface troposphere in Northeast China from 2003 to 2021, and combined with the national meteorological and field monitoring data, we studied the emission mechanism and emission source of surface methane in the permafrost area of Northeast China. Study results show that: the permafrost zone in Northeast China has a high methane emission capacity in four seasons, and the methane column concentration shows a significant double-peak seasonal variation. The first peak appears in summer (June to August) and the maximum appears in August, the second peak appears in winter (December to February) and the maximum appears in December. However, the maximum average growth rate of near-surface methane column concentration in Northeast China appeared in spring (5.378ppbv/a), the reason is that the carbon stored under the permafrost is gradually exposed and released in the form of methane. The emission sources include microbial action, methane transported by wetland groundwater, geological methane (metastable methane hydrate, steady-state methane hydrate and thermogenic methane produced in the deep underground or coal seams) stored in frozen layer. The study provide data and technical support for the estimation of carbon emissions in permafrost areas in Northeast China.