Stratosphere-troposphere exchange (STE) plays a crucial role in Earth's climate; however, the significance of small-scale processes such as midlatitude convection to global STE remains understudied. Midlatitude tropopause-overshooting convection is especially important to climate because it can enhance stratospheric water vapor, which has its greatest radiative forcing sensitivity in the extratropical lower stratosphere. Thus, it is essential to understand what factors influence the strength and prevalence of overshooting storms and associated STE. The U.S. Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) field campaign during 2021 and 2022 was the first large-scale airborne primarily focused on sampling stratospheric impacts from overshooting convection. Our research utilizes the extensive DCOTSS data set in combination with radar, satellite, and environmental observations to investigate relationships between observed stratosphere composition change and storm and environmental characteristics. Our results demonstrate greater magnitudes of STE for above-anvil cirrus plume (AACP)-producing storms and mesoscale convective systems (MCSs). In addition, the most extreme enhancements in water vapor and other tropospheric gases occur where the tropopause height is low and the depth of overshooting is high, especially for AACP-producing storms. We also investigate the impact of storm and environmental characteristics on pathways for hydration (air mass transport and mixing vs. ice sublimation), finding that they also modulate the frequencies of each process at different altitudes. Namely, mixing is found to be most prevalent in AACP-producing storms and MCSs, which can help explain transport differences between water vapor and other gases.

Overshooting storms are convective systems with updrafts that penetrate through the tropopause into the overlying stratosphere. These storms can rapidly transport a wide variety of chemical species and aerosols from the boundary layer and free troposphere directly to the stratosphere. The central plains of the U.S. and the Sierra Madre Occidental of Mexico are two of the global hotspots for overshooting convection. While the existence of these storms has been known for several decades, the amount of tropospheric air, including water vapor, trace gases, and aerosols, transported across the tropopause is poorly understood, as is their impact on the dynamics, chemistry, and radiative balance of the stratosphere. Climate models suggest that as Earth’s climate continues to warm, overshooting convection over the U.S. may increase, potentially causing changes to stratospheric composition and transport. To address these scientific questions, the NASA ER-2 high-altitude research aircraft flew 31 missions during the summers of 2021 and 2022 to make observations of the outflow from overshooting storms in the stratosphere over North America and the eastern Pacific Ocean as part of the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) project. The ER-2 carried a payload of 12 instruments to measure meteorological parameters, water and its isotopologues, trace gases, and aerosol properties. Ozone, water vapor, and aerosol sondes were also launched on balloons during the field deployments. This paper describes the science goals of the DCOTSS project, the aircraft measurement strategy, the data produced by the project, and highlights of science results to date.

cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision, and swath between coarse-resolution global mappers and fine-scale point-source imagers of methane, and for its airborne companion mission, MethaneAIR. MethaneSAT delivers hyperspectral data at an intermediate spatial resolution (similar to 100 & times; 400 m), whereas MethaneAIR provides even finer resolution (similar to 25 m), enabling the development of highly detailed maps of concentrations that enable quantification of both the sources and rates of emissions. In this study, we use machine learning methods to address the cloud and cloud shadow detection problem for sensors with these high spatial resolutions. Cloud and cloud shadows in remote sensing data need to be effectively screened out as they bias methane retrievals in remote sensing imagery and impact the quantification of emissions. We deploy and evaluate conventional techniques-including iterative logistic regression (ILR) and multilayer perceptron (MLP)-with advanced deep learning architectures, namely, U-Net and a spectral channel attention network (SCAN) method. Our results show that conventional methods struggle with spatial coherence and boundary definition, affecting the detection of clouds and cloud shadows. Deep learning models substantially improve detection quality: U-Net performs best in preserving spatial structure, while SCAN excels at capturing fine boundary details. Notably, SCAN surpasses U-Net on MethaneSAT data, underscoring the benefits of incorporating spectral attention for satellite-specific features. In addition, we combine the predictions of both U-Net and SCAN through a convolutional neural network (CNN). This ensemble method achieves the best performance on both MethaneAIR (F1: 78.50 +/- 3.08%) and MethaneSAT (F1: 78.80 +/- 1.28%) datasets, representing improvements of 2% and 10% over conventional methods (U-Net: 78.50 +/- 3.08% and 68.56 +/- 0.36% F1, respectively), while maintaining efficient inference (4.1 ms per 1000 km(2)). This in-depth assessment of various disparate machine learning techniques, as applied to MethaneSAT and MethaneAIR imaging spectroscopic data at varying spatial resolutions, demonstrates the strengths and effectiveness of advanced deep learning architectures in providing robust, scalable solutions for clouds and cloud shadow screening toward enhancing methane emission quantification capacity of existing and next-generation hyperspectral missions. Our data and code are publicly available at: https://doi.org/10.7910/DVN/IKLZOJ

More than a quarter of anthropogenic global warming has been attributed to methane growth in the atmosphere. Landfills account for 17% of estimated methane emissions in the United States of America (USA), according to the Environmental Protection Agency (EPA), but studies show that many landfills emit more than reported. We developed a novel method to calculate monthly methane emissions from an active landfill using atmospheric methane mixing ratios observed from a single tower in New Jersey, USA. The tower method provides two and a half years of semicontinuous measurements and therefore observes more of the variability of methane emissions and lacks the sampling bias present in other methods. Time-specific comparison of tower-based methane emissions against those observed from summertime aircraft sampling and year-round mobile ground-based platforms showed good agreement. Estimated methane emissions for 2023 were five times greater than those reported to the EPA. We observed a strong seasonality in methane emissions, with a peak in the winter and a minimum in the summer. This seasonal cycle was driven by a strong negative dependence on air temperature and the change in atmospheric pressure. Our results highlight the importance of observations in nonideal weather conditions (such as declining pressure and near-freezing temperatures) when methane emissions are largest. We suggest that this methodology could be applied to other suitable landfills to improve estimates of methane emissions.

Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantification. We present a machine learning framework for plume detection from MethaneSAT retrieved column-averaged dry-air mole fractions of methane. We address two core challenges: the scarcity of labeled MethaneSAT data and the need for inference reliability across diverse atmospheric and surface conditions. We first demonstrate that Mask R-CNN with a ResNet-50 backbone outperforms U-Net semantic segmentation on both MethaneAIR (an airborne version of MethaneSAT) and MethaneSAT data, with pixel-level F1 score gains of 10.49 and 5.48 respectively. To address MethaneSAT data scarcity, we evaluate three cross-sensor transfer strategies leveraging MethaneAIR flights and synthetic plumes. Mask R-CNN with ResNet-50 fine-tuned from MethaneAIR pre-trained weights is the most effective strategy, achieving instance-level precision of 0.60 and a near-perfect recall of 0.98 at the baseline operating point. A physics-informed post-processing pipeline converts detections into two operationally distinct modes. The first is a high-sensitivity mode that applies morphological filtering and proximity-based merging for comprehensive emission screening, achieving precision of 0.71 and recall of 0.94. The second is a high-precision mode that additionally applies a distribution-based classifier for confident source attribution, achieving precision of 0.92 and recall of 0.70. Manual review of detections classified as false positives against our wavelet-based ground truth labels reveals that a meaningful fraction of cases correspond to real methane enhancements excluded by conservative labeling criteria, indicating that precision values reported are lower bounds on true detection performance... Our data and code are available at: https://doi.org/10.7910/DVN/FR959H

Abstract. Strategies for mitigating methane emissions rely on understanding the underlying drivers of methane losses to the atmosphere. Observations of methane plumes emerging from point sources, combined with correct statistical interpretation, can provide key information. In this work, we examine a critical parameter, the probability of detection of a plume. For a given observing system, probability of detection is affected by the properties of the sensor, plume detection algorithm, observing conditions, and emission rate of the source. We parameterize relevant aspects of remotely sensed scenes containing plumes using a nondimensional observability parameter that predicts probability of detection. Our probability of detection model is trained using simulated plumes to capture natural variability in different meteorological conditions, and validated with data from controlled release experiments. We model probability of detection for two airborne imaging spectrometer systems, MethaneAIR and Insight M LeakSurveyorTM, and one high resolution satellite system, MethaneSAT. Monte Carlo simulations of emissions distributions implied by data from the extensive 2023 MAIRX campaign of MethaneAIR demonstrate the importance of an accurate probability of detection model, due to the heavy tailed emission distribution found in most oil and gas basins.

Abstract. Identifying and quantifying local methane emitters remains a major challenge for atmospheric monitoring. We present a novel top-down method to estimate both the upwind location and emission strength of an unknown atmospheric source from a time series of concentration observations. The approach employs backward trajectories from a Lagrangian Particle Dispersion Model (LPDM) to derive a characteristic transfer function for each potential source region. The transfer function that best reproduces the observed enhancement identifies the most likely source location. In a second step, the emission strength is inferred from the particle ensemble and its corresponding surface footprint. The method was developed and tested using data from a six-week measurement campaign in the San Francisco Bay Area, where six EM27/SUN near-infrared Fourier transform spectrometers were operated as part of a collaborative effort to quantify greenhouse gas emissions. At the UC Berkeley site, one instrument recorded a strictly periodic methane enhancement of approximately 10 ppb occurring every 12 minutes. Since co-emitted species showed no correlation with this pattern, the signal was attributed to a single, point-like, puff-emitting methane source. Favourable meteorological conditions enabled the analysis of several enhancement peaks. The retrieved average emission strength during the emission episodes was 0.8–78 g CH4 s-1 (equivalent to 2.1–190 metric tons yr-1). Although the exact source could not be identified in the field, the emission characteristics are consistent with periodic natural-gas venting from a heating system with an installed power output of approximately 500–1000 kW installed power. The study demonstrates the potential of this approach for detecting and characterising local methane emitters from ground-based remote-sensing observations.

Mitigation of methane emissions from the oil and gas sector is an effective way to reduce the near-term climate warming and losses of a valuable energy resource. The oil and gas value chain contributes at least 25 % of anthropogenic methane emissions globally and is the second largest methane-emitting sector in the United States. Here, we assess methane emissions in regions accounting for 70 % of US onshore oil and gas production in 2023 using data collected by the MethaneAIR airborne imaging spectrometer. We quantify total methane emissions across all observed regions to be similar to 9 (7.8-10) Tg yr(-1), with similar to 90 % of emissions estimated from the oil and gas sector (similar to 8 Tg yr(-1), equivalent to a methane loss rate of 1.6 % of gross gas production), which is about five times higher than reported by the US EPA. Both oil and gas emissions and gas production-normalized methane loss rates varied considerably by basin. Highly productive basins such as the Permian, Appalachian, and Haynesville-Bossier had the highest methane emissions (95-314 t h(-1)), whereas lower producing basins possibly associated with older infrastructure such as the Uinta and Piceance had higher loss rates (> 7 %). We found good agreement across total emissions quantified by MethaneAIR and other empirical and remote sensing estimates at national/basin/target-level scales. This work underscores the increasing value of remote sensing data for quantifying methane emissions, characterizing intensity of methane losses across the oil and gas sector, and mapping inter-basin emissions variability, which are all critical for tracking methane mitigation targets set by industry and governments.

The diel drawdown of CO2 provides a direct measure of light use efficiency and of the capacity of terrestrial ecosystems to mitigate climate change, as these processes influence how much carbon is absorbed by vegetation through photosynthesis. This study investigates CO2 drawdown in Northeastern U.S. forests by analyzing five years of column-averaged CO2 measurements (XCO2) from the EM27 instrument at the Harvard Forest and evaluating the performance of two vegetation models, including CarbonTracker and the Vegetation Photosynthesis and Respiration Model (VPRM), in representing CO₂ fluxes. EM27 XCO2 data reveal clear seasonal patterns with peaks in late spring and lows in late summer, reflecting an annual increase of approximately 3 ppm. We model XCO2 enhancements by convolving simulated footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model with biological CO2 fluxes from both vegetation models. Observed and modeled CO2 drawdown during the daytime all peak in the warmer months. VPRM drawdown rates closely align with observed data, showing a slight underestimation of peak values (-0.17 ppm hr-1 in July compared to -0.18 ppm hr-1 observed) in the average annual variation of daily XCO2 slopes. In contrast, CarbonTracker simulates weaker CO2 drawdown. The study highlights stable interannual variability in CO2 drawdown, with no indication of saturation in the ecosystems' CO2 drawdown capacity. In the context of climate change, this work underscores the value of long-term monitoring and modeling of XCO2 to track changes in CO2 drawdown and to identify environmental stresses affecting terrestrial carbon sinks.

The MethaneSAT satellite mission aims to detect, quantify, and monitor methane emissions in targets covering over 80% of global oil and gas production. Bridging the gap between existing point-source and global mapping remote sensing satellites, MethaneSAT enables simultaneous emission estimation from discrete sources and from dispersed sources. One of MethaneSAT's calibration activities is a monthly scan of the nearly full moon (a "lunar calibration scan"), which provides a consistent and well-known external light source in the wavelength range that MethaneSAT measures. This paper presents a detailed description of MethaneSAT's lunar calibration activities and example data from an early MethaneSAT lunar calibration scan. MethaneSAT uses a synchronous scanning mode where the spacecraft attitude is fixed with respect to the orbital frame and the instrument array scans perpendicular to the apparent velocity of the moon. MethaneSAT's instruments have a 21.3 field of view while the moon's angular diameter is 0.52, and so it would require 41 synchronous scans to illuminate the entire field of view. Due to data budget and time limitations MethaneSAT has been performing 5 lunar calibrations scans per lunar cycle when the phase angle is between +5 to +9 degrees. Observations from each lunar calibration scan are to be later compared to the Lunar Irradiance Model of the European Space Agency (LIME)'s disk-integrated lunar irradiance model, which is derived from ground-based observations from the Izaiia Atmospheric Observatory and Teide Peak. LIME predicts variations in lunar irradiance, accounting for view geometry, phase angle, lunar librations, and the lunar surface albedo distribution of maria and highlands. In August 2024, MethaneSAT successfully scanned the moon, demonstrating the viability of the proposed calibration approach. However, a larger dataset of monthly scans is necessary for trending analysis and comparison against the LIME model. As such, this paper focuses on the planning and methodology for lunar calibration, presenting an example scan as a proof of concept. Further calibration trend analyses will be conducted as more scans are collected in the coming months. Once sufficient data is available, the calibration will be finalized and integrated into satellite retrievals.

High-emitting methane point sources, quantified by remote sensing methods at individual facilities, have gained significant interest for enabling rapid monitoring and mitigation of methane emissions from the oil and gas sector. Here, we present new methane point source quantifications from MethaneAIR, the airborne precursor to MethaneSAT, from campaigns in 2021-2023, which targeted major oil and gas basins covering similar to 80 % of US onshore production. Flying at similar to 12 km above ground, MethaneAIR provides wide-area methane mapping and high-resolution measurements of high-emitting methane point sources. Across 13 major basins, MethaneAIR detected over 400 point sources with emission rates >similar to 150kgh(-1), for which we performed detailed attribution to facility categories within oil and gas and non-oil and gas sectors. In 2023, we quantified total point source methane emissions of 357 th(-1) (95 % confidence interval: 277-435 th(-1)), with similar to 80 % of the total attributable to oil and gas sources. Non-oil and gas sources made up 50 %-80 % of observed point source emissions in certain basins, with coal facilities in the Appalachian Basin being the largest source of non-oil and gas methane emissions (30-40 th(-1)). We observe emission source intermittency and significant variation across facility types and basins, highlighting the complex characteristics of high-emitting point sources. Our results emphasize the importance of detailed source attribution for prioritizing mitigation efforts and provide the first analysis of methane point sources in several regions, which will be improved by the observational capabilities of a growing set of methane satellites.

Reducing methane emissions from the oil and gas (oil–gas) sector has been identified as a critically important global strategy for reducing near-term climate warming. Recent measurements, especially by satellite and aerial remote sensing, underscore the importance of targeting the small number of facilities emitting methane at high rates (i.e., “super-emitters”) for measurement and mitigation. However, the contributions from individual oil–gas facilities emitting at low emission rates that are often undetected are poorly understood, especially in the context of total national- and regional-level estimates. In this work, we compile empirical measurements gathered using methods with low limits of detection to develop facility-level estimates of total methane emissions from the continental United States (CONUS) midstream and upstream oil–gas sector for 2021. We find that of the total 14.6 (12.7–16.8) Tg yr−1 oil–gas methane emissions in the CONUS for the year 2021, 70 % (95 % confidence intervals: 61 %–81 %) originate from facilities emitting <100kgh-1 and 30 % (26 %–34 %) and ∼80 % (68 %–90 %) originate from facilities emitting <10 and <200kgh-1, respectively. While there is variability among the emission distribution curves for different oil–gas production basins, facilities with low emissions are consistently found to account for the majority of total basin emissions (i.e., range of 60 %–86 % of total basin emissions from facilities emitting <100kgh-1). We estimate that production well sites were responsible for 70 % of regional oil–gas methane emissions, from which we find that the well sites that accounted for only 10 % of national oil and gas production in 2021 disproportionately accounted for 67 %–90 % of the total well site emissions. Our results are also in broad agreement with data obtained from several independent aerial remote sensing campaigns (e.g., MethaneAIR, Bridger Gas Mapping LiDAR, AVIRIS-NG (Airborne Visible/Infrared Imaging System – Next Generation), and Global Airborne Observatory) across five to eight major oil–gas basins. Our findings highlight the importance of accounting for the significant contribution of small emission sources to total oil–gas methane emissions. While reducing emissions from high-emitting facilities is important, it is not sufficient for the overall mitigation of methane emissions from the oil and gas sector which according to this study is dominated by small emission sources across the US. Tracking changes in emissions over time and designing effective mitigation policies should consider the large contribution of small methane sources to total emissions.

MethaneSAT is a joint American and New Zealand satellite mission, which involves partnership between the Environmental Defense Fund (EDF), MethaneSAT LLC and New Zealand government. MethaneSAT’s primary mission is to detect and quantify methane (CH4) emissions from both point and area sources from the global oil and gas production industry in support of emissions reductions. MethaneSAT will target specific 200 km x 200 km regions and map CH4 within those regions at 100 m x 400 m resolution with unprecedented precision. The Aotearoa New Zealand team’s aim is to develop and test the ability of the satellite to detect agricultural CH4 emissions. New Zealand is an ideal place to develop this capability due to its large CH4 emissions, 85% of which are from agricultural sources. We will present results of modelled atmospheric CH4 concentrations for agricultural targets in New Zealand, emission estimates from the agricultural targets and CH4 measurements collected during a shakedown field campaign, in preparation for the MethaneSAT launch in 2024.We use 1.5 km spatial resolution, New Zealand specific bottom-up CH4 fluxes and the Numerical Atmospheric dispersion Modelling Environment (NAME III), driven by meteorological input from the New Zealand Convective Scale Model (NZCSM, 1.5 km spatial resolution) Numerical Weather Prediction (NWP) model to create modelled agricultural XCH4 (column averaged) enhancements. The MethaneSAT-like targets are created for different scenarios to assess the changes in the XCH4 enhancements relative to meteorological conditions and bottom-up fluxes. We use the modelled agricultural XCH4 fields to test operational methods that are being developed for Level 4 products (i.e., emissions) in an Observing System Simulation Experiments (OSSE) framework and adapt them for diffuse agricultural sources. We will present results of modelled XCH4 scans for the main agricultural targets across New Zealand and the application of the MethaneSAT Level 4 methods (i.e., Geostatistical Inversion Framework, Divergence Integral Method) for agricultural sources.The 2023 New Zealand MethaneSAT pre-launch shakedown field campaign took place over ten days in Waikato, a region with New Zealand’s strongest agricultural CH4 emissions. The campaign involved the deployment of two EM27/SUN portable spectrometers and in situ CH4 samplers. One EM27/SUN was at a fixed location for the duration of the campaign, while the second instrument was positioned up or downwind to measure enhancements of XCH4. Four remote sites were used, with measurements collected on multiple occasions and under different meteorological conditions. Typical XCH4 enhancements of between 3 and 8 ppb were observed while side-by-side measurements with the two spectrometers yielded a minimum detection limit of 0.3 ppb. Ground based and airborne in situ measurements were also collected to provide additional context to the measured enhancements. The measured XCH4 enhancements aligned with agricultural XCH4 estimates from the modelling framework.

Nitric oxide (NO) in the remote marine atmosphere is underestimated by chemistry‐climate models. We explore the potential oceanic emissions of NO and its impacts using a global chemistry‐climate model with a newly developed oceanic NO emission inventory, considering the abiotic photochemical production of NO in the seawater. The results are evaluated using global observations from the NASA Atmospheric Tomography Mission (ATom). We propose that the photochemical production of NO from the photolysis of dissolved inorganic nitrite and nitrate in the surface seawater could lead to an oceanic NO x source of ∼3 Tg N per year, which could explain the missing NO in the remote marine boundary layer. This potential oceanic NO source leads to a small but potentially widespread increase in surface ozone and hydroxyl radicals in the remote marine environment, altering our understanding of the role the remote marine atmosphere plays in the global system. Further, the role of the photolysis of nitrate aerosols may be overestimated in recent studies and warrants further investigation.

The Total Carbon Column Observing Network (TCCON) measures column-average mole fractions of several greenhouse gases (GHGs), beginning in 2004, from over 30 current or past measurement sites around the world using solar absorption spectroscopy in the near-infrared (near-IR) region. TCCON GHG data have been used extensively for multiple purposes, including in studies of the carbon cycle and anthropogenic emissions, as well as to validate and improve observations from space-based sensors. Here, we describe an update to the retrieval algorithm used to process the TCCON near-IR solar spectra and to generate the associated data products. This version, called GGG2020, was initially released in April 2022. It includes updates and improvements to all steps of the retrieval, including but not limited to the conversion of the original interferograms into spectra, the spectroscopic information used in the column retrieval, post hoc air mass dependence correction, and scaling to align with the calibration scales of in situ GHG measurements. All TCCON data are available through https://tccondata.org/ (last access: 22 April 2024) and are hosted on CaltechDATA (https://data.caltech.edu/, last access: 22 April 2024). Each TCCON site has a unique DOI for its data record. An archive of all the sites' data is also available with the DOI https://doi.org/10.14291/TCCON.GGG2020 (Total Carbon Column Observing Network (TCCON) Team, 2022). The hosted files are updated approximately monthly, and TCCON sites are required to deliver data to the archive no later than 1 year after acquisition. Full details of data locations are provided in the “Code and data availability” section.

Land surface models diverge in their predictions of the Amazon forest's response to climate change-induced droughts, with some showing a catastrophic collapse of forests, while others simulating resilience. Therefore, observations of tropical ecosystem responses to real-world droughts and other extreme events are needed. We report long-term seasonal dynamics of photosynthesis, respiration, net carbon exchange, phenology, and tree demography and characterize the effect of dry and wet events on ecosystem form and function at the Tapajós National Forest, Brazil, using over two decades of eddy covariance observations that include the 2015–2016 El Niño drought and La Niña 2008–2009 wet periods. We found strong forest responses to both ENSO events: La Niña saw forest net carbon loss from reduced photosynthesis (due to lower incoming radiation from increased cloudiness) even as ecosystem respiration (Reco) was maintained at mean seasonal levels. El Niño induced the opposite short-term effect, net carbon gains, despite significant reductions in photosynthesis (from a drought-induced halving of canopy conductance to CO2 and significant losses of leaf area), because drought suppression of Reco losses was even greater. However, long-term responses to the two climate perturbations were very different: transient during La Niña –the forest returned to its “normal” state as soon as the climate did, and long-lasting during El Niño –leaf area loss and associated declines in photosynthetic capacity (Pc) and canopy conductance were exacerbated and extended by feedbacks from higher temperatures and atmospheric evaporative demand and persisted for ∼3+ years after normal rainfall resumed. These findings indicate that these forests are more vulnerable to drought than to excess rain, because drought drives significant changes in forest structure (e.g., leaf-abscission and mortality) and ecosystem function (e.g. reduced stomatal conductance). As future Amazonian climate change increases frequencies of hydrological extremes, these mechanisms will determine the long-term fate of tropical forests.

Large wildfires can generate pyrocumulonimbus (pyroCb) clouds that transport substantial amounts of smoke into the upper troposphere and lower stratosphere (UT/LS), perturbing aerosol budget and properties in these regions. Despite projections of increasing pyroCb events in the future, their climate impact, particularly the radiative forcing of smoke aerosols, remains poorly constrained, primarily due to limited direct measurements. Here we present aircraft measurements of aerosols and gases within 5-day-old pyroCb smoke plumes from a New Mexico wildfire, sampled at altitudes of 14-15 km. The aerosols, primarily organic biomass burning particles, exhibited an unusually large number-mode diameter of 500-600 nm. Microphysical simulations suggest that such large aerosols can form through combinations of cloud processing and coagulation in the relatively stable UT/LS environment. These large pyroCb aerosols increase outgoing radiation (aerosol-only perturbation) by 30-36% compared to typical non-pyroCb smoke aerosols with mode diameters of 200-300 nm, causing an instantaneous enhancement in cooling of the atmospheric column. Many climate models use smaller aerosol sizes for smoke than those observed for pyroCb aerosols, potentially underestimating the radiative cooling effects of pyroCb events. With a rising prevalence of pyroCb aerosols in a warming climate, accurately representing their size and optical properties in climate models is crucial.

Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at https://doi.org/10.5281/zenodo.10734299 (Omara, 2024).

Reducing methane (CH4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20 x 20 m(2) pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100-150 kg h-1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (similar to 20 x 20 m(2)) mapped over large swaths (similar to 100 x 100 km(2)) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (similar to 130 x 400 m(2) pixel size over a target area measuring similar to 200 x 200 km(2)), thereby delivering quantitative data on basin-wide methane emissions.