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Background: Products of conception samples are often collected and analyzed to try to determine the cause of an early pregnancy loss. However, sample collection may not always be possible, and maternal cell contamination and culture failure can affect the analysis. Cell-free DNA-based analysis of a blood sample could be used as an alternative method in early pregnancy loss cases to detect if aneuploidies were present in the fetus. Methods: In this prospective study, blood samples from early pregnancy loss patients were analyzed for the presence of fetal aneuploidies using a modified version of a noninvasive prenatal testing assay for cell-free DNA analysis. Results from cell-free DNA analysis were compared against the gold standard, microarray analysis of products of conception samples. This study was registered with ClinicalTrials.gov, identifier: NCT04935138. Results: Of the 76 patient samples included in the final study cohort, 11 were excluded from performance calculations. The 65 patient samples included in the final analysis included 49 with an abnormal microarray result and 16 with a normal microarray result. Based on results from these 65 samples, the study found that genome-wide cell-free DNA analysis had a sensitivity of 73.5% with a specificity of 100% for the detection of fetal aneuploidies in early pregnancy loss cases. Conclusions: This prospective study provides further support for the utility of cell-free DNA analysis in detecting fetal aneuploidies in early pregnancy loss cases. This approach could allow for a noninvasive method of investigating the etiology of miscarriages to be made available clinically.

Abstract Small water bodies (i.e., ponds; 0.01 km2). We leverage high‐resolution (3 m) optical satellite imagery from Planet Labs and deep learning methods to map seasonal changes in pond and lake areal extent across four regions in Alaska. Our water maps indicate that changes in open water extent over the snow‐free season are especially pronounced in ponds. To investigate potential impacts of seasonal changes in pond area on carbon emissions, we provide a case study of open water methane emission budgets using the new water maps. Our approach has widespread applications for water resources, habitat and land cover change assessments, wildlife management, risk assessments, and other biogeochemical modeling efforts.

Measurement(s) carbon dioxide • methane • carbon monoxide Technology Type(s) spectroscopy Sample Characteristic - Environment city Sample Characteristic - Location North America

Abstract Arctic warming is affecting snow cover and soil hydrology, with consequences for carbon sequestration in tundra ecosystems. The scarcity of observations in the Arctic has limited our understanding of the impact of covarying environmental drivers on the carbon balance of tundra ecosystems. In this study, we address some of these uncertainties through a novel record of 119 site-years of summer data from eddy covariance towers representing dominant tundra vegetation types located on continuous permafrost in the Arctic. Here we found that earlier snowmelt was associated with more tundra net CO2 sequestration and higher gross primary productivity (GPP) only in June and July, but with lower net carbon sequestration and lower GPP in August. Although higher evapotranspiration (ET) can result in soil drying with the progression of the summer, we did not find significantly lower soil moisture with earlier snowmelt, nor evidence that water stress affected GPP in the late growing season. Our results suggest that the expected increased CO2 sequestration arising from Arctic warming and the associated increase in growing season length may not materialize if tundra ecosystems are not able to continue sequestering CO2 later in the season.

Sector-based methane emissions can be backed out from observed methane fluxes, using a Bayesian optimal estimation method. This could help with monitoring gas leaks from industry.

Abstract Wetland methane (CH4) emissions comprise about one‐third of the global CH4 source. The latitudinal distribution and climate sensitivity of wetland CH4 fluxes are the key determinants of the global CH4‐climate feedback. However, large differences exist between bottom‐up estimates, informed by ground‐based flux measurements, and top‐down estimates derived from spaceborne total column CH4. Despite the extensive coverage of satellite CH4 concentration observations, challenges remain with using top‐down estimates to test bottom‐up models, mainly because of the uncertainties in the satellite retrievals, the model representation errors, the variable prior emissions, and the confounding role of the posterior error covariance structures. Here, we use satellite‐based top‐down CH4 flux estimates (2010–2012) to test and refine 42 bottom‐up estimates of wetland emissions that use a range of hypothesized wetland extents and process controls. Our comparison between bottom‐up models and satellite‐based fluxes innovatively accounts for cross‐correlations and spatial uncertainties typically found in top‐down inverse estimates, such that only the information from satellite observations and the atmospheric transport model is kept as a constraint. We present a satellite‐constrained wetland CH4 ensemble product derived from assembling the highest‐performance bottom‐up models, which estimates global wetland CH4 emissions of 148 (117–189, 5th–95th percentile) Tg CH4 yr−1. We find that tropical wetland emissions contribute 72% (63%–85%) to the global wetland total. We also find that a lower‐than‐expected temperature sensitivity agrees better with atmospheric CH4 measurements. Overall, our approach demonstrates the potential for using satellites to quantitatively refine bottom‐up wetland CH4 emission estimates, their latitudinal distributions, and their sensitivity to climate.

Abstract Anthropogenic carbon dioxide (CO2) emissions dominate uncertainties in the global carbon budget. Global inventories, such as the National Greenhouse Gas Inventories, have latencies of 12–24 months and may not keep pace with rapidly changing infrastructure, particularly in the developing world. Our work reveals that airborne and satellite imaging spectrometers provide 3–30 m spatial resolution and accurate quantification of CO2 emissions at the facility scale. Examples from 17 coal and gas fired power plants across the United States demonstrate robust correlation and 21% agreement on average between our remotely sensed estimates and simultaneous in situ measured emissions. We highlight four examples of coal‐fired power plants in India, Poland, and South Korea, where we quantify significant carbon dioxide emissions from power plants where limited public emissions data exist. Leveraging previous work on methane (CH4) plume detection, we present a strategy to exploit joint CO2 and CH4 plume imaging to quantify carbon emissions across widely distributed industrial infrastructure, including facilities that co‐emit CO2 and CH4. We show an example of a coal operation, where we attribute 25% of greenhouse gas emissions to coal extraction (CH4) and the remaining 75% to energy generation (CO2). Satellite spectrometers could track high emitting coal‐fired power plants that collectively contribute to 60% or more of global coal CO2 emissions. Multiple revisits and coordinated targeting of these high emitting facilities by multiple spaceborne instruments will be key to reducing uncertainties in global anthropogenic CO2 emissions and supporting emissions mitigation strategies.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegetation. AIM-North would consist of two satellites in a highly elliptical orbit formation, observing over land from ∼40°N to 80°N multiple times per day. Each satellite would carry a near-infrared to shortwave infrared imaging spectrometer for CO2, CH4, and CO, and an ultraviolet-visible imaging spectrometer for air quality. Both instruments would measure solar-induced fluorescence from vegetation. A cloud imager would make near-real-time observations, which could inform the pointing of the other instruments to focus only on the clearest regions. Multiple geostationary (GEO) AQ and GHG satellites are planned for the 2020s, but they will lack coverage of northern regions like the Arctic. AIM-North would address this gap with quasi-geostationary observations of the North and overlap with GEO coverage to facilitate intercomparison and fusion of these datasets. The resulting data would improve our ability to forecast northern air quality and quantify fluxes of GHG and AQ species from forests, permafrost, biomass burning and anthropogenic activity, furthering our scientific understanding of these processes and supporting environmental policy.

Recent climate change has induced widespread soil thawing and permafrost degradation in the Tibetan Plateau. Significant advances have been made in better characterizing Tibetan Plateau soil freeze/thaw dynamics, and their interaction with local-scale ecohydrological processes. However, factors such as sparse networks of in-situ sites and short observational period still limit our understanding of the Tibetan Plateau permafrost. Satellite-based optical and infrared remote sensing can provide information on land surface conditions at high spatial resolution, allowing for better representation of spatial heterogeneity in the Tibetan Plateau and further infer the related permafrost states. Being able to operate at “all-weather” conditions, microwave remote sensing has been widely used to retrieve surface soil moisture, freeze/thaw state, and surface deformation, that are critical to understand the Tibetan Plateau permafrost state and changes. However, coarse resolution (>10 km) of current passive microwave sensors can add large uncertainties to the above retrievals in the Tibetan Plateau area with high topographic relief. In addition, current microwave remote sensing methods are limited to detections in the upper soil layer within a few centimetres. On the other hand, algorithms that can link surface properties and soil freeze/thaw indices to permafrost properties at regional scale still need improvements. For example, most methods using InSAR (interferometric synthetic aperture radar) derived surface deformation to estimate active layer thickness either ignore the effects of vertical variability of soil water content and soil properties, or use site-specific soil moisture profiles. This can introduce non-negligible errors when upscaled to the broader Tibetan Plateau area. Integrating satellite remote sensing retrievals with process models will allow for more accurate representation of Tibetan Plateau permafrost conditions. However, such applications are still limiting due to a number of factors, including large uncertainties in current satellite products in the Tibetan Plateau area, and mismatch between model input data needs and information provided by current satellite sensors. Novel approaches to combine diverse datasets with models through model initialization, parameterization and data assimilation are needed to address the above challenges. Finally, we call for expansion of local-scale observational network, to obtain more information on deep soil temperature and moisture, soil organic carbon content, and ground ice content.

Postoperative adhesions represent a frequent complication of abdominal surgery. Adhesions can result from infection, ischemia, and foreign body reaction, but commonly develop after any surgical procedure. The morbidity caused by adhesions affects quality of life and, therefore, it is paramount to continue to raise awareness and scientific recognition of the burden of adhesions in healthcare and clinical research. This 2021 Global Expert Consensus Group worked together to produce consented statements to guide future clinical research trials and advise regulatory authorities. It is critical to harmonize the expectations of research, to both develop and bring to market improved anti-adhesion therapies, with the ultimate, shared goal of improved patient outcomes.

Beavers have established themselves as a key component of low arctic ecosystems over the past several decades. Beavers are widely recognized as ecosystem engineers, but their effects on permafrost-dominated landscapes in the Arctic remain unclear. In this study, we document the occurrence, reconstruct the timing, and highlight the effects of beaver activity on a small creek valley confined by ice-rich permafrost on the Seward Peninsula, Alaska using multi-dimensional remote sensing analysis of satellite (Landsat-8, Sentinel-2, Planet CubeSat, and DigitalGlobe Inc./MAXAR) and unmanned aircraft systems (UAS) imagery. Beaver activity along the study reach of Swan Lake Creek appeared between 2006 and 2011 with the construction of three dams. Between 2011 and 2017, beaver dam numbers increased, with the peak occurring in 2017 (n = 9). Between 2017 and 2019, the number of dams decreased (n = 6), while the average length of the dams increased from 20 to 33 m. Between 4 and 20 August 2019, following a nine-day period of record rainfall (>125 mm), the well-established dam system failed, triggering the formation of a beaver-induced permafrost degradation feature. During the decade of beaver occupation between 2011 and 2021, the creek valley widened from 33 to 180 m (~450% increase) and the length of the stream channel network increased from ~0.6 km to more than 1.9 km (220% increase) as a result of beaver engineering and beaver-induced permafrost degradation. Comparing vegetation (NDVI) and snow (NDSI) derived indices from Sentinel-2 time-series data acquired between 2017 and 2021 for the beaver-induced permafrost degradation feature and a nearby unaffected control site, showed that peak growing season NDVI was lowered by 23% and that it extended the length of the snow-cover period by 19 days following the permafrost disturbance. Our analysis of multi-dimensional remote sensing data highlights several unique aspects of beaver engineering impacts on ice-rich permafrost landscapes. Our detailed reconstruction of the beaver-induced permafrost degradation event may also prove useful for identifying degradation of ice-rich permafrost in optical time-series datasets across regional scales. Future field- and remote sensing-based observations of this site, and others like it, will provide valuable information for the NSF-funded Arctic Beaver Observation Network (A-BON) and the third phase of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) Field Campaign.

We quantify hourly on-road fossil fuel carbon dioxide (FFCO2) emissions at the road segment level for the Los Angeles (LA) megacity based on observed traffic data, and characterize emission patterns across space and time. This on-road FFCO2 emissions dataset for LA (from Hestia version 1.0), based on actual traffic volume, provides emissions per vehicle kilometer travelled (VKT)—an important metric for greenhouse gas (GHG) reductions. We further identify emissions hotpots that can help state and local policy makers plan the most effective GHG reduction strategies. On-road vehicle traffic accounts for half of the FFCO2 emissions in LA, of which 41% is from arterials (intermediate road type). Arterials also have the largest C emissions intensity—FFCO2 per VKT—possibly from high traffic congestion and fleet composition. Non-interstate emissions hotspots (> 419 tC lane-km-1) are equally dominated by arterials and collectors (the lowest road type) in terms of FFCO2 emissions though collectors have a higher VKT. These hotspots occur in densely populated areas and developed landuse classes, largely in LA (67%) and Orange (18%) counties, and provide specific targets for emissions reduction efforts. The estimated uncertainties for interstate, arterial and collector emissions per road length are ± 2.1, ± 0.5 and ± 18.0%, respectively. Our overall estimates compare reasonably well with other products, DARTE and FIVE but with substantial differences in spatial distribution. The method for developing this dataset is easily replicable in other urban landscapes, and represents a powerful tool for carbon cycle science and regional policy makers.

Abstract Analyzing retrieval accuracy and precision is an important element of space‐based CO2 retrievals. However, this error analysis is sometimes challenging to perform rigorously because of the subtlety of Multivariate Statistics. To help address this issue, we revisit some fundamentals of Multivariate Statistics that help reveal the statistical essence of the associated error analysis. We show that the related statistical methodology is useful for revealing the intrinsic discrepancy and relation between the retrieval error for a nonzero‐variate CO2 state and that for a zero‐variate one. Our study suggests that the two scenarios essentially yield the same‐magnitude accuracy, while the latter scenario yields a better precision than the former. We also use this methodology to obtain a rigorous framework systematically and explore a broadly used approximate framework for analyzing CO2 retrieval errors. The approximate framework introduces errors due to an essential, but often forgotten, fact that a priori climatology in reality is never equal to the true state. Due to the nature of the problem considered, realistic numerical simulations that produce synthetic spectra may be more appropriate than remote sensing data for our specific exploration. As highlighted in our retrieval simulations, utilizing the approximate framework may not be universally satisfactory in assessing the accuracy and precision of Xco2 retrievals (with errors up to 0.17–0.28 ppm and 1.4–1.7 ppm, respectively, at SNR = 400). In situ measurements of CO2 are needed to further our understanding of this issue and related implications.

Northern high-latitude lakes are critical sites for carbon processing and serve as potential conduits for the emission of permafrost-derived carbon and greenhouse gases. However, the fate and emission pathways of permafrost carbon in these systems remain uncertain. Here, we used the natural abundance of radiocarbon to identify and trace the predominant sources of methane, carbon dioxide, dissolved inorganic and organic carbon in nine lakes within the Yukon Flats National Wildlife Refuge in interior Alaska, a discontinuous permafrost region with high landscape heterogeneity and susceptibility to climate, permafrost, and hydrological changes. We find that although Yukon Flats lakes primarily process young carbon (modern to 1290 ± 60 years before present), permafrost-derived carbon is present in some of the sampled lakes and contributes, at most, 30 ± 10% of the dissolved carbon in lake surface waters. Apportionment of young carbon and legacy carbon (carbon with radiocarbon age ⩾5000 years before present) is decoupled among the dissolved inorganic and organic carbon species, with methane showing a stronger legacy signature. Our observations suggest that permafrost-thaw-related transport of carbon through Yukon Flats lacustrine ecosystems and into the atmosphere is small, and likely regulated by surficial sediments, permafrost distribution, wildfire occurrence, or masked by contemporary carbon processes. The heterogeneity of lakes across our study area and northern landscapes more broadly cautions against using any one region (e.g. Yedoma permafrost lakes) to upscale their contribution across the pan-Arctic.

Rapid warming in Arctic tundra may lead to drier soils in summer and greater lightning ignition rates, likely culminating in enhanced wildfire risk. Increased wildfire frequency and intensity leads to greater conversion of permafrost carbon to greenhouse gas emissions. Here, we quantify the effect of recent tundra fires on the creation of methane (CH _4 ) emission hotspots, a fingerprint of the permafrost carbon feedback. We utilized high-resolution (∼25 m ^2 pixels) and broad coverage (1780 km ^2 ) airborne imaging spectroscopy and maps of historical wildfire-burned areas to determine whether CH _4 hotspots were more likely in areas burned within the last 50 years in the Yukon–Kuskokwim Delta, Alaska, USA. Our observations provide a unique observational constraint on CH _4 dynamics, allowing us to map CH _4 hotspots in relation to individual burn events, burn scar perimeters, and proximity to water. We find that CH _4 hotspots are roughly 29% more likely on average in tundra that burned within the last 50 years compared to unburned areas and that this effect is nearly tripled along burn scar perimeters that are delineated by surface water features. Our results indicate that the changes following tundra fire favor the complex environmental conditions needed to generate CH _4 emission hotspots. We conclude that enhanced CH _4 emissions following tundra fire represent a positive feedback that will accelerate climate warming, tundra fire occurrence, and future permafrost carbon loss to the atmosphere.

Forest structure—the height, cover, vertical complexity, and spatial patterns of trees—is a key indicator of productivity variation across forested extents. During the 2017 and 2019 growing seasons, NASA’s Arctic-Boreal Vulnerability Experiment collected full-waveform airborne LiDAR using the land, vegetation and imaging sensor, sampling boreal and tundra landscapes across a variety of ecological regions from central Canada westward through Alaska. Here, we compile and archive a geo-referenced gridded suite of these data that include vertical structure estimates and novel horizontal cover estimates of vegetation canopy cover derived from vegetation’s vertical LiDAR profile. We validate these gridded estimates with small footprint airborne LiDAR, and link >36 million of them with stand age estimates from a Landsat time-series of tree-canopy cover that we confirm with plot-level disturbance year data. We quantify the regional magnitude and variability in site index, the age-dependent rates of forest growth, across 15 boreal ecoregions in North America. With this open archive suite of forest structure data linked to stand age, we bound current forest productivity estimates across a boreal structure gradient whose response to key bioclimatic drivers may change with stand age. These results, derived from a reduction of a large archive of airborne LiDAR and a Landsat time series, quantify forest productivity bounds for input into forest and ecosystem growth models, to update forecasts of changes in North America’s boreal forests by improving the regional parametrization of forest growth rates.

Remote sensing and site-level observations have shown unprecedented changes in Arctic ecosystems owing to climate warming. These observations include greening and browning trends in Arctic vegetation as well as changes in species composition both in the tundra and the boreal forests. Here, we leveraged solar induced chlorophyll fluorescence (SIF) to study changes in ecosystem phenology across the pan-Arctic domain from 2000 to 2020. Ecoregions, as a proxy for plants’ functional traits, were the single most important variable to explain the spatial and phenological heterogeneity in observed SIF trends. We observed unique regional trends in ecosystems responses to climate change affecting the timing of spring photosynthesis onset, magnitude of peak productivity during the growing season (GS) and fall senescence. While, Photosynthetic activity in the early GS showed increasing trends across the vast majority of the pan-Arctic, it tends to decline at the end of the season for nearly half of the land area, including parts of North America but more significantly in central Siberia. The observed changes in phenology highlight the role of biodiversity in regional climate sensitivity and emphasizes the need for better representations of sub-biomes to community level information in Arctic ecosystem process models and projections. These results also highlight the importance of phenology information in ecosystem models for better understanding of the timing and magnitude of carbon uptake in the Arctic domain with accelerated changes in climate.

Estimating the impacts of climate change on the global carbon cycle relies on projections from Earth system models (ESMs). While ESMs currently project large warming in the high northern latitudes, the magnitude and sign of the future carbon balance of Arctic-Boreal ecosystems are highly uncertain. The new generation of increased complexity ESMs in the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) is intended to improve future climate projections. Here, we benchmark the Coupled Model Intercomparison Project (CMIP) 5 and 6 (8 CMIP5 members and 12 CMIP6 members) with the International Land Model Benchmarking (ILAMB) tool over the region of NASA’s Arctic-Boreal vulnerability experiment (ABoVE) in North America. We show that the projected average net biome production (NBP) in 2100 from CMIP6 is higher than that from CMIP5 in the ABoVE domain, despite the model spread being slightly narrower. Overall, CMIP6 shows better agreement with contemporary observed carbon cycle variables (photosynthesis, respiration, biomass) than CMIP5, except for soil carbon and turnover time. Although both CMIP ensemble members project the ABoVE domain will remain a carbon sink by the end of the 21st century, the sink strength in CMIP6 increases with CO _2 emissions. CMIP5 and CMIP6 ensembles indicate a tipping point defined here as a negative inflection point in the NBP curve by 2050–2080 independently of the shared socioeconomic pathway (SSP) for CMIP6 or representative concentration pathway (RCP) for CMIP5. The model ensembles therefore suggest that, if the carbon sink strength keeps declining throughout the 21st century, the Arctic-Boreal ecosystems in North America may become a carbon source over the next century.

We studied geomorphological controls on methane (CH _4 ) hotspots in the Mackenzie Delta region in northern Canada using airborne imaging spectroscopy collected as part of the Arctic Boreal Vulnerability Experiment. Methane emissions hotspots were retrieved at ∼25 m ^2 spatial resolution from a ∼10 000 km ^2 NASA’s Next Generation Airborne Visible/Infrared Imaging Spectrometer survey of the Mackenzie Delta acquired 31 July–3 August 2017. Separating the region into the permafrost plateau and the lowland delta, we refined the domain wide power law of CH _4 enhancements detected as a function of distance to standing water in different ecoregions. We further studied the spatial decay of the distance to water relationship as a function of land cover across the Delta. We show that geomorphology exerts a strong control on the spatial patterns of emissions at regional to sub-regional scales: compared to methane hotspots detected in the upland, we find that methane hotspots detected in the lowland have a more gradual power law curve indicating a weaker spatial decay with respect to distance from water. Spatial decay of CH _4 hotspots in uplands is more than 2.5 times stronger than in lowlands, which is due to differences in topography and geomorphological influence on hydrology. We demonstrate that while the observed spatial distributions of CH _4 follow expected trends in lowlands and uplands, these quantitatively complement knowledge from conventional wetland and freshwater CH _4 mapping and modeling.

Methane (CH _4 ), an important greenhouse gas and pollutant, has been targeted for mitigation. Our recent California airborne survey identified >500 CH _4 point source super-emitters, which accounted for 34%–46% of the statewide CH _4 emissions inventory for 2016 (Duren et al 2019 Nature 575 180–184). Individual plumes were observed in close proximity to expected methane emitting infrastructure, including gas storage facilities, hydrocarbon storage tanks, landfills, dairy lagoons, and pipeline leaks. In order to systematically attribute these plumes to their sources, we developed Vista-CA a geospatial database, that contains more than 900 000 validated CH _4 infrastructure elements in the state of California. In parallel, we developed a complimentary algorithm that attributes any individual CH _4 plume observation to the most likely Vista-CA source with 99% accuracy. The present study illustrates the capabilities of the Vista-CA CH _4 database along with the Airborne Visible/Infrared Imaging Spectrometer—Next Generation airborne CH _4 retrievals to locate and attribute CH _4 point sources to specific economic sectors to improve the state CH _4 budget and identify mitigation targets.

Temperature is a dominant factor driving arctic and boreal ecosystem phenology, including leaf budburst and gross primary production (GPP) onset in Alaskan spring. Previous studies hypothesized that both accumulated growing degree day (GDD) and cold temperature (chilling) exposure are important to leaf budburst. We test this hypothesis by combining both satellite and aircraft vegetation measurements with the Community Land Model Version 4.5 (CLM), in which the end of plant dormancy depends on thermal conditions (i.e. GDD). We study the sensitivity of GPP onset of different Alaskan deciduous vegetation types to a GDD model with chilling requirement (GC model) included. The default CLM simulations have a 1–12 d earlier day of year GPP onset over Alaska vegetated regions compared to satellite constrained estimates from the Polar Vegetation Photosynthesis and Respiration Model. Integrating a GC model into CLM shifts the phase and amplitude of GPP. During 2007–2016, mean GPP onset is postponed by 5 ± 7, 4 ± 8, and 1 ± 6 d over Alaskan northern tundra, shrub, and forest, respectively. The GC model has the greatest impact during warm springs, which is critical for predicting phenology response to future warming. Overall, spring GPP high bias is reduced by 10%. Thus, including chilling requirement in thermal forcing models improves northern high-latitude phenology, but leads to other impacts during the growing season which require further investigation.

Accurate and timely detection, quantification, and attribution of methane emissions from Underground Gas Storage (UGS) facilities is essential for improving confidence in greenhouse gas inventories, enabling emission mitigation by facility operators, and supporting efforts to assess facility integrity and safety. We conducted multiple airborne surveys of the 12 active UGS facilities in California between January 2016 and November 2017 using advanced remote sensing and in situ observations of near-surface atmospheric methane (CH _4 ). These measurements where combined with wind data to derive spatially and temporally resolved methane emission estimates for California UGS facilities and key components with spatial resolutions as small as 1–3 m and revisit intervals ranging from minutes to months. The study spanned normal operations, malfunctions, and maintenance activity from multiple facilities including the active phase of the Aliso Canyon blowout incident in 2016 and subsequent return to injection operations in summer 2017. We estimate that the net annual methane emissions from the UGS sector in California averaged between 11.0 ± 3.8 GgCH _4 yr ^−1 (remote sensing) and 12.3 ± 3.8 GgCH _4 yr ^−1 ( in situ ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 ± 3.2 GgCH _4 yr ^−1 (remote sensing) and 9.5 ± 3.2 GgCH _4 yr ^−1 ( in situ ), in both cases around 5 times higher than reported. The majority of methane emissions from UGS facilities in this study are likely dominated by anomalous activity: higher than expected compressor loss and leaking bypass isolation valves. Significant variability was observed at different time-scales: daily compressor duty-cycles and infrequent but large emissions from compressor station blow-downs. This observed variability made comparison of remote sensing and in situ observations challenging given measurements were derived largely at different times, however, improved agreement occurred when comparing simultaneous measurements. Temporal variability in emissions remains one of the most challenging aspects of UGS emissions quantification, underscoring the need for more systematic and persistent methane monitoring.

Solid waste management represents one of the largest anthropogenic methane emission sources. However, precise quantification of landfill and composting emissions remains difficult due to variety of site-specific factors that contribute to landfill gas generation and effective capture. Remote sensing is an avenue to quantify process-level emissions from waste management facilities. The California Methane Survey flew the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) over 270 landfills and 166 organic waste facilities repeatedly during 2016–2018 to quantify their contribution to the statewide methane budget. We use representative methane retrievals from this campaign to present three specific findings where remote sensing enabled better landfill and composting methane monitoring: (1) Quantification of strong point source emissions from the active face landfills that are difficult to capture by in situ monitoring or landfill models, (2) emissions that result from changes in landfill infrastructure (design, construction, and operations), and (3) unexpected large emissions from two organic waste management methods (composting and digesting) that were originally intended to help mitigate solid waste emissions. Our results show that remotely-sensed emission estimates reveal processes that are difficult to capture in biogas generation models. Furthermore, we find that airborne remote sensing provides an effective avenue to study the temporally changing dynamics of landfills. This capability will be further improved with future spaceborne imaging spectrometers set to launch in the 2020s.

NASA has launched the decade-long Arctic-Boreal Vulnerability Experiment (ABoVE). While the initial phases focus on field and airborne data collection, early integration with modeling activities is important to benefit future modeling syntheses. We compiled feedback from ecosystem modeling teams on key data needs, which encompass carbon biogeochemistry, vegetation, permafrost, hydrology, and disturbance dynamics. A suite of variables was identified as part of this activity with a critical requirement that they are collected concurrently and representatively over space and time. Individual projects in ABoVE may not capture all these needs, and thus there is both demand and opportunity for the augmentation of field observations, and synthesis of the observations that are collected, to ensure that science questions and integrated modeling activities are successfully implemented.