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Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to differences in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling related artifacts. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of 6-year (2010?2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three data sets combined. Combining the data sets results in more precise flux estimates for subcontinental regions relative to any of the data sets alone. Combining the data sets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.33?0.56?ppm) in comparison to GOSAT (0.37?0.61?ppm), TCCON (0.50?0.68?ppm), or in situ and flask measurements (0.46?0.56?ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve constraints on regional fluxes. This stands in contrast with many earlier attempts to combine these data sets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

The contribution of soil heterotrophic respiration to the boreal-Arctic carbon (CO2) cycle and its potential feedback to climate change remain poorly quantified. We developed a remote sensing driven permafrost carbon model at intermediate scale (~ 1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics, and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below surface. Model outputs include soil temperature profiles and carbon fluxes at 1-km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in predicting net ecosystem CO2 exchange (NEE) in both tundra (R > 0.8, RMSE = 0.34 g C m−2 d−1) and boreal forest (R > 0.73, RMSE = 0.51 g C m−2 d−1). Benchmark assessments using two regional in-situ datasets indicate that the model captures the complex influence of snow insulation on soil temperature, and the temperature sensitivity of cold-season soil respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil carbon emissions. Earlier snow melt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil respiration is closely linked to the number of snow-free days after land surface freezes (R = −0.48, p 2 seasonal cycle, especially during the early cold season at 10-km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of boreal-Arctic carbon cycle.

Accurately characterizing isoprene ozonolysis continues to challenge atmospheric chemists. The reaction is believed to be a spontaneous, concerted cycloaddition. However, little information is available about the entrance channel and isoprene-ozone complexes thought to define the long-range portion of the reaction coordinate. Our coupled cluster and auxiliary field quantum Monte Carlo calculations predict multiple stable isoprene-ozone van der Waals complexes for the trans-isoprene in the gas-phase with moderate association energies. These results indicate that long-range dynamics in the isoprene-ozone entrance channel can impact the overall reaction in the troposphere and provide the spectroscopic information necessary to extend microwave characterization of isoprene ozonolysis to pre-reactive complexes. At the air-water interface, Born-Oppenheimer Molecular Dynamics simulations indicate that the cycloaddition reaction between ozone and trans-isoprene follows a stepwise mechanism, which is quite distinct from our proposed gas-phase mechanism and occurs on a femtosecond time scale. The stepwise nature of isoprene ozonolysis on the aqueous surface is more consistent with the DeMore mechanism than with the Criegee mechanism suggested by the gas-phase calculations, suggesting that the reaction media may play an important role in the reaction. Overall, these predictions aim to provide a missing fundamental piece of molecular insight into isoprene ozonolysis, which has broad tropospheric implications due to its critical role as a nighttime source of hydroxyl radical.

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 (CH4). 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 GgCH4 yr−1 (remote sensing) and 12.3 ± 3.8 GgCH4 yr−1 (in situ). Net annual methane emissions for the 7 facilities that reported emissions in 2016 were estimated between 9.0 ± 3.2 GgCH4 yr−1 (remote sensing) and 9.5 ± 3.2 GgCH4 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.

The Methane Source Finder is a web-based data portal developed under NASA’s CMS and ACCESS programs for exploring methane data in the state of California. This open access interactive map allows users to discover, analyze, and download data across a range of spatial scales derived from remote-sensing, surface monitoring, and bottom-up infrastructure information. This includes methane plume images and associated emission estimates derived from the 2016-2018 California Methane Survey using the airborne imaging spectrometer AVIRIS-NG. The fine spatial resolution (typically 3 m) AVIRIS-NG products when combined with the Vista infrastructure database of over 270,000 components statewide permits direct attribution of emissions to individual point source locations. These point source products have benefited from evaluation and feedback from state and local agencies and private sector companies and in some cases were used to directly guide leak detection and repair efforts. Additional data layers at local and regional scales provide context for point source emissions. These include methane flux inversions for the Los Angeles basin derived from surface observations and tracer transport modeling (3 km, 4 day resolution) as well as the CMS US methane gridded inventory (10 km, monthly resolution) over the state of California.

The 2016-2018 California Methane Survey used the airborne imaging spectrometer AVIRIS-NG to survey approximately 59,000 km2 and 272,000 individual facilities and infrastructure components. Over 500 strong methane point sources spanning the waste management, agriculture, and energy sectors were detected, geolocated, and quantified. In order to facilitate communication of results with scientists, stakeholder agencies in California, private sector companies, and the public, we developed the Methane Source Finder web-based data portal. This state of the art Earth science data visualization tool allows users to discover, analyze, and download data across a range of spatial scales derived from remote-sensing, surface monitoring, and bottom-up infrastructure information. In this presentation, we will highlight our overall science findings from the California Methane Survey and provide a number of examples where observed methane plumes were used to directly guide leak detection and repair efforts. Future plans include expanding the data portal beyond California and incorporating regional scale flux inversions derived from satellite observations. Methane Source Finder supports methane research (e.g., multi-scale synthesis), enables facility-scale mitigation, and improves public awareness of greenhouse gas emissions.

Methane emissions monitoring is rapidly expanding with increasing coverage of surface, airborne, and satellite instruments. However, no single methane instrument or observing strategy can both close emission budgets and pinpoint point sources on regional to global scales. Instead, we present a multi-tiered data analytics system that synthesizes information across various instruments into a single analytic framework. We highlight an example in Los Angeles, where we combine surface measurements from the Los Angeles megacities project, mountaintop measurements from the CLARS-FTS instrument, airborne AVIRIS-NG point source emission estimates, and TROPOMI total column retrievals into a single analytic framework. Surface, mountaintop, and satellite measurements are assimilated into a methane flux inverse model to constrain basin-wide emissions and pinpoint sub-basin methane hotspots. We show an example of a large urban landfill, whose anomalous emissions were detected by the inverse system, and validated using AVIRIS-NG methane plume maps. This general approach of quantifying both methane area and point source emissions is an avenue not just for closing regional to global scale budgets, but also for understanding which emission sources dominate the budget (i.e., so called methane super-emitters). We finally show how this multi-tiered analytic framework can be improved with future satellite missions, and present examples of unexpectedly large methane emissions that were detected by a new generation of satellite imaging spectrometers.

With recent increases in burned area within boreal forests that have been linked to climate warming, there is a need to better understand the composition of emissions and their impact on atmospheric composition. Most previous studies have estimated boreal fire emission factors from daytime samples collected via aircraft near fire plumes or at the surface near actively burning fires. Here we quantified emission factors for CO and CH4 from a massive regional fire complex in interior Alaska during the summer of 2015 using continuous high-resolution trace gas observations from the CRV tower (Fox, AK). Averaged over the 2015 fire season, the CO/CO2 emission ratio was 0.138±0.048 and the CO emission factor was 145±50 g CO per kg of dry biomass consumed. The CO/CO2 emission ratio was about 35 % higher and more variable than most previous aircraft-based estimates for fresh wildfire emissions. The mean CH4/CO2 emission ratio was 0.010±0.003 and the CH4 emission factor was 6.05±2.09 g CH4 per kg of dry biomass consumed, with means similar to previous reports. CO and CH4 emission factors varied in synchrony, with higher CH4 emission factors observed during periods with lower modified combustion efficiency (MCE). By coupling a fire emissions inventory with an atmospheric model, we identified that at least 35 individual fires contributed to trace gas variations measured at the CRV tower, representing a significant increase in sampling compared to the number of boreal fires measured in all previous boreal forest fire work. The model also indicated that typical mean transit times between trace gas emission and tower measurement were 1–3 days, indicating that the time series sampled combustion across day and night burning phases. The high and variable CO emission factor estimates reported here provide evidence for a more prominent role of smoldering combustion, highlighting the importance of continuously sampling of fires across time-varying environmental conditions that are representative of typical burning conditions.

We present the current development of the Carbon Balance Observatory (CARBO). CARBO is a wide-swath mapping, low Earth orbit (LEO) new generation of instruments that expands on the ground-breaking CO2 and Solar Induced Fluorescence (SIF) measurements pioneered by the Orbiting Carbon Observatory (OCO-2/3) by adding CH4 and CO detection. The instrument’s spatial coverage is delivered at 2 km by 2 km resolution with a field-of-view of 10° to 15° from LEO for a ~200 km wide swath. It achieves roughly 20x better spatial coverage than the OCO-2 instrument, and 3x better Solar Induced Chlorophyll Fluorescence (SIF) detection sensitivity, in a smaller package. CARBO will measure CO2 at

The springtime transition to regional-scale onset of photosynthesis and net ecosystem carbon uptake in boreal and tundra ecosystems are linked to the soil freeze-thaw state. We present evidence from diagnostic and inversion models constrained by satellite fluorescence and airborne CO2 from 2012 to 2014 indicating the timing and magnitude of spring carbon uptake in Alaska correlates with landscape thaw and ecoregion. Landscape thaw in boreal forests typically occurs in late April (DOY 111 ± 7) with a 29 ± 6 day lag until photosynthetic onset. North Slope tundra thaws 3 weeks later (DOY 133 ± 5) but experiences only a 20 ± 5 day lag until photosynthetic onset. These time lag differences reflect efficient cold season adaptation in tundra shrub and the longer dehardening period for boreal evergreens. Despite the short transition from thaw to photosynthetic onset in tundra, synchrony of tundra respiration with snow melt and landscape thaw delays the transition from net carbon loss (at photosynthetic onset) to net uptake by 13 ± 7 days, thus reducing the tundra net carbon uptake period. Two global CO2 inversions using a CASA-GFED model prior estimate earlier northern high latitude net carbon uptake compared to our regional inversion, which we attribute to (i) early photosynthetic-onset model prior bias, (ii) inverse method (scaling factor + optimization window), and (iii) sparsity of available Alaskan CO2 observations. Another global inversion with zero prior estimates the same timing for net carbon uptake as the regional model but smaller seasonal amplitude. The analysis of Alaskan eddy covariance observations confirms regional scale findings for tundra, but indicates that photosynthesis and net carbon uptake occur up to 1 month earlier in evergreens than captured by models or CO2 inversions, with better correlation to above-freezing air temperature than date of primary thaw. Further collection and analysis of boreal evergreen species over multiple years and at additional subarctic flux towers are critically needed.

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

Remote spectroscopy of terrestrial ecosystems generally falls into one of two categories: UV, Visible and ShortWave InfraRed (VSWIR) methods measuring reflected solar illumination, and Thermal InfraRed (TIR) methods measuring radiation emitted from the Earth's surface. Both interpret the measured radiance with inversion algorithms that estimate surface reflectance, emissivity, surface temperatures, and the atmospheric state. However, the two regimes have traditionally used independent inversion methods. Here we present the first simultaneous inversion of the upwelling radiance spectrum from the UV through the VSWIR and the Thermal Infrared. Maximum A Posteriori estimation determines the surface and atmosphere state that is most consistent with measured radiance across the entire range. We find that the complementary information in the two intervals improves estimates of atmospheric properties, surface emissivity, and surface temperature. Posterior uncertainty is 40% lower in surface temperature and 60% in air temperature. Spurious correlations between atmospheric and surface state are also reduced, with 90% lower correlated uncertainty between total column water vapor and both surface and air temperature. Temperature-emissivity separation also improves, with a 33% reduction in correlated posterior uncertainty between the parameters. We demonstrate the method on coincident data from the Classic Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) operating in the VSWIR range, and the Hyperspectral Thermal Emission Spectrometer (HyTES) in the TIR range, during flights on NASA's high-altitude ER-2 aircraft. Measurements spanning 0.4 to 12 microns can improve estimates of surface and atmospheric properties, significantly advancing studies in terrestrial ecosystems, geology, agriculture, and urban management.

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.

Study Objective To develop a new endometriosis classification system for scoring intraoperative surgical complexity and to examine its correlation with patient-reported pain and infertility. Design Multicenter study of patients treated at 3 recognized endometriosis centers. Setting Three specialized endometriosis surgical centers in Sao Paulo, Brazil and Barcelona, Spain. Patients Patients aged 15 to 45 years with histologically proven endometriosis and no history of pelvic malignancy underwent laparoscopic treatment of endometriosis. Interventions Demographic data and clinical history, including dysmenorrhea, noncyclic pelvic pain, dyspareunia, dysuria and dyschezia, were prospectively recorded. All patients were staged surgically according to the new 2021 American Association of Gynecologic Laparoscopists (AAGL) and revised American Society of Reproductive Medicine (ASRM) classification systems. The staging for each system was compared against a 4-level surgical complexity scale defined by the most complex procedure performed. Measurements and Main Results A total of 1224 patients undergoing surgery met inclusion criteria. The AAGL score discriminated between 4 stages of surgical complexity with high reproducibility (κ = 0.621), whereas the ASRM score discriminated between the complexity stages with poor reproducibility (κ = 0.317). The AAGL staging system correlated with dysmenorrhea, dyspareunia, dyschezia, total pain score, and infertility comparably with the ASRM staging system. Conclusion The AAGL 2021 Endometriosis Classification allows for identifying objective intraoperative findings that reliably discriminate surgical complexity levels better than the ASRM staging system. The AAGL severity stage correlates comparably with pain and infertility symptoms with the ASRM stage.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

The purpose of this review is to understand the minimally invasive approach to the excision and repair of an isthmocele.Previous small trials and case reports have shown that the minimally invasive approach by hysteroscopy and/or laparoscopy can cure symptoms of a uterine isthmocele, including abnormal bleeding, pelvic pain and secondary infertility. A recent larger prospective study has been published that evaluates outcomes of minimally invasive isthmocele repair. Smaller studies and individual case reports echo the positive results of this larger trial.The cesarean section scar defect, also known as an isthmocele, has become an important diagnosis for women who present with abnormal uterine bleeding, pelvic pain and secondary infertility. It is important for providers to be aware of the effective surgical treatment options for the symptomatic isthmocele. A minimally invasive approach, whether it be laparoscopic or hysteroscopic, has proven to be a safe and effective option in reducing symptoms and improving fertility. VIDEO ABSTRACT: http://links.lww.com/COOG/A37.

Changes in climate and fire regimes are transforming the boreal forest, the world’s largest biome. Boreal North America recently experienced two years with large burned area: 2014 in the Northwest Territories and 2015 in Alaska. Here we use climate, lightning, fire and vegetation data sets to assess the mechanisms contributing to large fire years. We find that lightning ignitions have increased since 1975, and that the 2014 and 2015 events coincided with a record number of lightning ignitions and exceptionally high levels of burning near the northern treeline. Lightning ignition explained more than 55% of the interannual variability in burned area, and was correlated with temperature and precipitation, which are projected to increase by mid-century. The analysis shows that lightning drives interannual and long-term ignition and burned area dynamics in boreal North America, and implies future ignition increases may increase carbon loss while accelerating the northward expansion of boreal forest. The boreal forest is being transformed by changes in its climate–fire regime. Analysis now shows that lightning drives year-to-year and long-term ignition and burned area trends in boreal North America.

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.

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.

Methane (CH4) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH4 fluxes across Alaska for 2012-2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH4 observations at the state-wide, multi-year scale more effectively than global-scale, state-of-the-art process-based models. This result points to a simple and effective way of representing CH4 flux patterns across Alaska. It further suggests that contemporary process-based models can improve their representation of key processes that control fluxes at regional scales, and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH4 observations. In addition, we find that CH4 emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.44 Tg CH4 (for May-Oct.). Contemporary global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland area in regions without visible surface water, and these models prematurely shut down CH4 fluxes at soil temperatures near 0°C. As a consequence, wetlands covered by vegetation and wetlands with persistently cold soils could be larger contributors to natural CH4 fluxes than in process estimates. Lastly, we find that the seasonality of CH4 fluxes varied during 2012-2014, but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation; year-to-year variability in these environmental conditions did not affect obvious changes in total CH4 fluxes from the state.

Terahertz absorption spectroscopy was employed to measure the ground state pure rotational transitions of the water isotopologue HD 18 O . A total of 105 pure rotational transitions were observed in the 0.5–5.0 THz region with ∼ 100 kHz accuracy for the first time. The observed positions were fit to experimental accuracy using the Euler series expansion of the asymmetric-top Hamiltonian together with the literature Microwave, Far-IR and IR data in the ground state and ν 2 . The new measurements and predictions reported here support the analysis of astronomical observations by high-resolution spectroscopic telescopes such as SOFIA and ALMA where laboratory rest frequencies with uncertainties of 1 MHz or less are required for proper analysis of velocity resolved astrophysical data.

California methane (CH4) emissions are quantified for three years from two tower networks and one aircraft campaign. We used backward trajectory simulations and a mesoscale Bayesian inverse model, ...

Currently, the community lacks capabilities to assess and monitor landscape scale permafrost active layer dynamics over large extents. To address this need, we developed a concept of a remote sensing based Soil Inversion Model for regional Permafrost (SIM-P) monitoring. The current SIM-P framework includes a satellite-based soil process model and a soil dielectric model. We are also working on incorporating a radar scattering model for Arctic tundra into the SIM-P framework. A unified soil parameterization scheme was developed to harmonize key soil thermal, hydraulic and dielectric parameters in the soil process and radar models that can be used in the joint soil-radar inversion framework. The soil parameter retrievals of the SIM-P framework include soil organic content (SOC) and active layer thickness (ALT). Initial tests of SIM-P using in-situ soil permittivity observations showed reasonable accuracy in predicting site-level SOC and soil temperature profiles at an Alaska tundra site and ALT in Arctic Alaska. SIM-P will be further tested using airborne P- and L-band radar data collected during NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) to evaluate the sensitivity of longwave radar to active layer properties.

We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (EnMAP, PRISMA, EMIT, SBG) scheduled for launch in 2019–2025. These instruments are designed to map the Earth's surface with a sampling distance as fine as 30 × 30 m2 but they have spectral resolution of 7–10 nm in the 2200–2400 nm band that should also allow useful detection of atmospheric methane. We simulate scenes viewed by EnMAP (10 nm spectral resolution, 180 signal-to-noise ratio) using the EnMAP End-to-End Simulation Tool with superimposed methane plumes generated by large-eddy simulations. We retrieve atmospheric methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estimation algorithm. We find an EnMAP precision of 4–13 % for atmospheric methane depending on surface type, allowing effective single-pass detection of 100+ kg h−1 methane point sources depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resolution. We simulated the EnMAP capability with actual plume observations over oil/gas fields in California from the airborne AVIRIS-NG sensor (3 × 3 m2 pixel resolution, 5 nm spectral resolution, SNR 200–400). We spectrally and spatially downsampled AVIRIS-NG images to match EnMAP instrument specifications and found that we could successfully detect point sources of ~ 100 kg h−1 over bright surfaces. Estimated emission rates inferred with a generic Integrated Mass Enhancement (IME) method agreed within a factor of 2 between EnMAP and AVIRIS-NG. Better agreement may be achieved with a more customized IME method. Our results suggest that imaging spectrometers in space could play a transformative role in the future for quantifying methane emissions from point sources on a global scale.

We combine sustained observations from a network of atmospheric monitoring stations with inverse modeling to uniquely obtain spatiotemporal (3‐km, 4‐day) estimates of methane emissions from the Los Angeles megacity and the broader South Coast Air Basin for 2015–2016. Our inversions use customized and validated high‐fidelity meteorological output from Weather Research Forecasting and Stochastic Time‐Inverted Lagrangian model for South Coast Air Basin and innovatively employ a model resolution matrix‐based metric to disentangle the spatiotemporal information content of observations as manifested through estimated fluxes. We partially track and constrain fluxes from the Aliso Canyon natural gas leak and detect closure of the Puente Hills landfill, with no prior information. Our annually aggregated fluxes and their uncertainty excluding the Aliso Canyon leak period lie within the uncertainty bounds of the fluxes reported by the previous studies. Spatially, major sources of CH_4 emissions in the basin were correlated with CH_4‐emitting infrastructure. Temporally, our findings show large seasonal variations in CH_4 fluxes with significantly higher fluxes in winter in comparison to summer months, which is consistent with natural gas demand and anticorrelated with air temperature. Overall, this is the first study that utilizes inversions to detect both enhancement (Aliso Canyon leak) and reduction (Puente Hills) in CH_4 fluxes due to the unintended events and policy decisions and thereby demonstrates the utility of inverse modeling for identifying variations in fluxes at fine spatiotemporal resolution.

Context Elagolix is an oral gonadotropin-releasing hormone (GnRH) antagonist recently approved for the treatment of endometriosis-associated pain and being developed for heavy menstrual bleeding associated with uterine fibroids. Objective The objective was to evaluate the effects of elagolix on ovulation and ovarian sex hormones. Design and Setting This was a randomized, open-label, multicenter study. Participants Participants were healthy ovulatory women aged 18 to 40 years. Interventions Elagolix was administered orally for 3 continuous 28-day dosing intervals at 100 to 200 mg once daily (QD), 100 to 300 mg twice daily (BID), and 300 mg BID plus estradiol/norethindrone acetate (E2/NETA) 1/0.5 mg QD. Main Outcome Measures The main outcomes measures were ovulation rates measured by transvaginal ultrasound, progesterone concentrations, and hormone suppression. Results Elagolix suppressed ovulation in a dose-dependent manner. The percentage of women who ovulated was highest at 100 mg QD (78%), intermediate at 150 and 200 mg QD and 100 mg BID (47%–57%), and lowest at 200 and 300 mg BID (32% and 27%, respectively). Addition of E2/NETA to elagolix 300 mg BID further suppressed the ovulation rate to 10%. Elagolix also suppressed luteinizing hormone and follicle stimulating hormone in a dose-dependent manner, leading to dose-dependent suppression of estradiol and progesterone. Elagolix had no effect on serum biomarker of ovarian reserve, and reduced endometrial thickness compared to the screening cycle. Conclusion Women being treated with elagolix may ovulate and should use effective methods of contraception. The rate of ovulation was lowest with elagolix 300 mg BID plus E2/NETA 1/0.5 mg QD.

The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R>0.55, p) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼<0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77, p); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper ( m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (>0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss.

Study Objective To determine whether incorporation of operative hysteroscopy with biopsy of products of conception, in conjunction with a suction curettage for a first trimester missed abortion, affected the rate of maternal cell contamination when chromosomal analysis was performed on the products of conception, and to determine the rates of retained products of conception with incorporation of hysteroscopy after suction curettage. Design Retrospective chart study. Setting Private, minimally invasive surgery and infertility practice with academic-community hospital affiliation. Patients Infertility patients undergoing evacuation of products of conception for documented first trimester miscarriages between 2006 and 2017. Interventions Suction curettage or hysteroscopic biopsy and suction curettage, followed by chromosomal analysis of products of conception for determination of fetal genetics. Measurements and Results A total of 264 charts were analyzed. Patients were categorized into 2 groups based on surgical collection of products of conception: group 1 (N = 174), suction curettage only, and group 2 (N = 90), a single procedure consisting of operative hysteroscopy with biopsy of products of conception followed by suction curettage and then diagnostic hysteroscopy to look for retained products. Data for chromosome detection and retained products of conception were available for 246 and 239 patients, respectively. No significant differences were detected between the groups for age, body mass index, ethnicity, gravida, parity, primary infertility, secondary infertility, spontaneous conception, single or multiple gestation, and surgical complications. Fetal chromosome detection was significantly higher without maternal contamination in group 2 (88.5%) compared with group 1 (64.8%) (p Conclusion Obtaining fetal genetics can be useful when planning for a future successful pregnancy. The addition of operative hysteroscopy to biopsy the gestational sac, chorionic villi, and/or fetus significantly decreases the risk of maternal contamination and increases the ability to detect fetal chromosomes for genetic analysis without an increased risk of surgical complications. Despite the low risk of surgical complications, immediate second-look hysteroscopy after the completion of suction evacuation does not reduce the risk of postoperative retained products of conception.

Methane is a powerful greenhouse gas and is targeted for emissions mitigation by the US state of California and other jurisdictions worldwide

Cities generate 70% of anthropogenic greenhouse gas emissions, a fraction that is growing with global urbanization. While cities play an important role in climate change mitigation, there has been little focus on reducing urban methane emissions. Here we develop a conceptual framework for methane mitigation in cities by describing emission processes, the role of measurements, and a need for new institutional partnerships. Urban methane emissions are likely to grow with expanding use of natural gas and organic waste disposal systems in growing population centers; however, we currently lack the ability quantify this increase. We also lack systematic knowledge of the relative contribution of these distinct source sectors on emissions. We present new observations from 4 North American cities to demonstrate that methane emissions vary in magnitude and sector from city to city, and hence require different mitigation strategies. Detections of fugitive emissions from these systems suggest that current mitigation approaches are absent or ineffective. These findings illustrate that tackling urban methane emissions will require research efforts to identify mitigation targets, develop and implement new mitigation strategies, and monitor atmospheric methane levels to ensure the success of mitigation efforts. This research will require a variety of techniques to achieve these objectives, and should be deployed in cities globally. We suggest that metropolitan-scale partnerships may effectively coordinate systematic measurements and actions focused on emission reduction goals.

Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10–30 % to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH4 predictions and site-level observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October–April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, cold-season CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

This study reports the results from analyzing a number of high resolution, high signal-to-noise ratio (S/N) spectra in the 2.06-μm spectral region for pure CO2 and mixtures of CO2 in dry air. A multispectrum nonlinear least squares curve fitting technique has been used to retrieve the various spectral line parameters. The dataset includes 27 spectra: ten pure CO2, two 99% 13C-enriched CO2 and fifteen spectra of mixtures of 12C-enriched CO2 in dry air. The spectra were recorded at various gas sample temperatures between 170 and 297 K. The absorption path lengths range from 0.347 to 49 m. The sample pressures for the pure CO2 spectra varied from 1.1 to 594 Torr; for the two 13CO2 spectra the pressures were ∼10 and 146 Torr. For the air-broadened spectra, the pressures of the gas mixtures varied between 200 and 711 Torr with CO2 volume mixing ratios ranging from 0.014% to 0.203%. The multispectrum fitting technique was applied to fit simultaneously all these spectra to retrieve consistent set of line positions, intensities, and line shape parameters including their temperature dependences; for this, the Voigt line shape was modified to include line mixing (via the relaxation matrix formalism) and quadratic speed dependence. The new results are compared to select published values, including recent ab initio calculations. These results are required to retrieve the column averaged dry air mole fraction (XCO2) from space-based observations, such as the Orbiting Carbon Observatory-2 (OCO-2) satellite mission that NASA launched in July 2014.

Megacities are major sources of anthropogenic fossil fuel CO2 (FFCO2) emissions. The spatial extents of these large urban systems cover areas of 10 000 km2 or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO2 emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO2 emission product, Hestia-LA, to simulate atmospheric CO2 concentrations across the LA megacity at spatial resolutions as fine as ∼ 1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO2 emission products to evaluate the impact of the spatial resolution of the CO2 emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO2 concentrations. We find that high spatial resolution in the fossil fuel CO2 emissions is more important than in the atmospheric model to capture CO2 concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO2 fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO2 fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO2 concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO2 emissions monitoring in the LA megacity requires FFCO2 emissions modelling with ∼ 1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

Pressure-broadened line shapes in the 30013←00001 (ν1+4 ν 2 0 +ν3) band of 12C16O2 at 6228 cm−1 are reanalyzed using new spectra recorded with sample temperatures down to 170 K. High resolution, high signal-to-noise (S/N) laboratory measurements of line shapes (Lorentz air- and self-broadened half-width coefficients, pressure-shift coefficients and off-diagonal relaxation matrix element coefficients) as a function of gas sample temperatures for various pressures and volume mixing ratios are presented. The spectra were recorded using two different Fourier transform spectrometers (FTS): (1) the McMath-Pierce FTS located at the National Solar Observatory on Kitt Peak, Arizona (and reported in Devi et al., J Mol Spectrosc 2007;245:52-80) and, (2) the Bruker IFS-125HR FTS at the Jet Propulsion Laboratory in Pasadena, California. The 19 spectra taken at Kitt Peak were all recorded near room temperature while the 27 Bruker spectra were acquired both at room temperature and colder temperatures (170-296 K). Various spectral resolutions (0.004–0.011 cm−1), absorption path lengths (2.46–121 m) and CO2 samples (natural and 12C-enriched) were included in the dataset. To maximize the accuracies of the various retrieved line parameters, a multispectrum nonlinear least squares spectrum fitting software program was used to adjust the ro-vibrational constants (G,B,D etc.) and intensity parameters (including Herman-Wallis terms) instead of directly measuring the individual line positions and intensities. To minimize systematic residuals, line mixing (via off-diagonal relaxation matrix elements) and quadratic speed dependence parameters were included in the analysis. Contributions from other weakly absorbing bands: the 30013←00001 and 30012←00001 bands of 13C16O2, the 30013←00001 band of 12C16O18O, hot bands 31113←01101 and 32212←02201 of 12C16O2, as well as the 40013←10001 and the 40014←10002 bands of 12C16O2, present within the fitted interval were also measured. Results from previous works and new calculations are compared to present measurements, where appropriate.

Continuous measurements of atmospheric methane (CH4) mole fractions measured by NOAA's Global Greenhouse Gas Reference Network in Barrow, AK (BRW), show strong enhancements above background values when winds come from the land sector from July to December from 1986 to 2015, indicating that emissions from arctic tundra continue through autumn and into early winter. Twenty-nine years of measurements show little change in seasonal mean land sector CH4 enhancements, despite an increase in annual mean temperatures of 1.2 ± 0.8°C/decade (2σ). The record does reveal small increases in CH4 enhancements in November and December after 2010 due to increased late-season emissions. The lack of significant long-term trends suggests that more complex biogeochemical processes are counteracting the observed short-term (monthly) temperature sensitivity of 5.0 ± 3.6 ppb CH4/°C. Our results suggest that even the observed short-term temperature sensitivity from the Arctic will have little impact on the global atmospheric CH4 budget in the long term if future trajectories evolve with the same temperature sensitivity.

Remote sensing of the atmosphere is typically achieved through measurements that are high-resolution radiance spectra. In this article, our goal is to characterize the first-moment and second-moment properties of the errors obtained when solving the regularized inverse problem associated with space-based atmospheric CO2 retrievals, specifically for the dry air mole fraction of CO2 in a column of the atmosphere. The problem of estimating (or retrieving) state variables is usually ill posed, leading to a solution based on regularization that is often called Optimal Estimation (OE). The difference between the estimated state and the true state is defined to be the retrieval error; error analysis for OE uses a linear approximation to the forward model, resulting in a calculation where the first moment of the retrieval error (the bias) is identically zero. This is inherently unrealistic and not seen in real or simulated retrievals. Nonzero bias is expected since the forward model of radiative transfer is strongly nonlinear in the atmospheric state. In this article, we extend and improve OE's error analysis based on a first-order, multivariate Taylor series expansion, by inducing the second-order terms in the expansion. Specifically, we approximate the bias through the second derivative of the forward model, which results in a formula involving the Hessian array. We propose a stable estimate of it, from which we obtain a second-order expression for the bias and the mean square prediction error of the retrieval.

Satellite CO_2 retrievals from the Greenhouse gases Observing SATellite (GOSAT), Atmospheric Infrared Sounder (AIRS), and Tropospheric Emission Spectrometer (TES) and in situ measurements from the National Oceanic and Atmospheric Administration - Earth System Research Laboratory (NOAA-ESRL) Surface CO_2 and Total Carbon Column Observing Network (TCCON) are utilized to explore the CO_2 variability at different altitudes. A multiple regression method is used to calculate the CO_2 annual cycle and semiannual cycle amplitudes from different data sets. The CO_2 annual cycle and semiannual cycle amplitudes for GOSAT X_(CO2) and TCCON X_(CO2) are consistent but smaller than those seen in the NOAA-ESRL surface data. The CO_2 annual and semiannual cycles are smallest in the AIRS midtropospheric CO_2 compared with other data sets in the Northern Hemisphere. The amplitudes for the CO_2 annual cycle and semiannual cycle from GOSAT, TES, and AIRS CO_2 are small and comparable to each other in the Southern Hemisphere. Similar regression analysis is applied to the Model for OZone And Related chemical Tracers-2 and CarbonTracker model CO_2. The convolved model CO_2 annual cycle and semiannual cycle amplitudes are similar to those from the satellite CO_2 retrievals, although the models tend to underestimate the CO_2 seasonal cycle amplitudes in the Northern Hemisphere midlatitudes and underestimate the CO_2 semiannual cycle amplitudes in the high latitudes. These results can be used to better understand the vertical structures for the CO_2 annual cycle and semiannual cycle and help identify deficiencies in the models, which are very important for the carbon budget study.

Rare earth elements are known to occur in low concentrations in U.S. coals and coal byproducts. These low concentrations may make rare earth element recovery from these materials unattractive, using only physical separation techniques. However, given the significant production of rare earths through ion exchange extraction in China, two U.S. coal byproducts were examined for ion extraction, using ammonium sulfate, an ionic liquid, and a deep eutectic solvent as lixiviants. Extraction of rare earth elements in each case produced high recoveries of rare earth elements to the solution. This suggests that in at least the cases of the materials examined, U.S. coal byproducts may be technically suitable as REE ores. More work is required to establish economic suitability.

Space‐based remote sensing of the column‐averaged methane dry air mole fraction (XCH_4) has greatly increased our understanding of the spatiotemporal patterns in the global methane cycle. The potential to retrieve multiple pieces of vertical profile information would further improve the quantification of CH_4 across space‐time scales. We conduct information analysis for channel selection and evaluate the prospects of retrieving multiple pieces of information as well as total column CH_4 from both ground‐based and space‐based near‐infrared remote sensing spectra. We analyze the degrees of freedom of signal (DOF) in the CH_4 absorption bands near 2.3 μm and 1.6 μm and select ∼1% of the channels that contain >95% of the information about the CH_4 profile. The DOF is around 4 for fine ground‐based spectra (resolution = 0.01 cm^(−1)) and 3 for coarse space‐based spectra (resolution = 0.20 cm^(−1)) based on channel selection and a signal‐to‐noise ratio (SNR) of 300. The DOF varies from 2.2 to 3.2 when SNR is between 100 and 300, and spectral resolution is 0.20 cm^(−1). Simulated retrieval tests in clear‐sky conditions using the selected channels reveal that the retrieved partial column‐averaged CH_4 values are not sensitive to the a priori profiles and can reflect local enhancements of CH_4 in different partial air columns. Both the total and partial column‐averaged retrieval errors in all tests are within 1% of the true state. These simulated tests highlight the possibility to retrieve up to three to four pieces of information about the vertical distribution of CH_4 in reality.

Author(s): Mouteva, GO; Czimczik, CI; Fahrni, SM; Wiggins, EB; Rogers, BM; Veraverbeke, S; Xu, X; Santos, GM; Henderson, J; Miller, CE; Randerson, JT | Abstract: Black carbon (BC) aerosol emitted by boreal fires has the potential to accelerate losses of snow and ice in many areas of the Arctic, yet the importance of this source relative to fossil fuel BC emissions from lower latitudes remains uncertain. Here we present measurements of the isotopic composition of BC and organic carbon (OC) aerosols collected at two locations in interior Alaska during the summer of 2013, as part of NASA's Carbon in Arctic Reservoirs Vulnerability Experiment. We isolated BC from fine air particulate matter (PM2.5) and measured its radiocarbon (Δ14C) content with accelerator mass spectrometry. We show that fires were the dominant contributor to variability in carbonaceous aerosol mass in interior Alaska during the summer by comparing our measurements with satellite data, measurements from an aerosol network and predicted concentrations from a fire inventory coupled to an atmospheric transport model. The Δ14C of BC from boreal fires was 131 ± 52‰ in the year 2013 when the Δ14C of atmospheric CO2 was 23 ± 3‰, corresponding to a mean fuel age of 20 years. Fire-emitted OC had a similar Δ14C (99 ± 21‰) as BC, but during background (low fire) periods OC (45 to 51‰) was more positive than BC (-354 to -57‰). We also analyzed the carbon and nitrogen elemental and stable isotopic composition of the PM2.5. Fire-emitted aerosol had an elevated carbon to nitrogen (C/N) ratio (29 ± 2) and δ15N (16 ± 4‰). Aerosol Δ14C and δ13C measurements were consistent with a mean depth of burning in organic soil horizons of 20 cm (and a range of 8 to 47 cm). Our measurements of fire-emitted BC and PM2.5 composition constrain the end-member of boreal forest fire contributions to aerosol deposition in the Arctic and may ultimately reduce uncertainties related to the impact of a changing boreal fire regime on the climate system.