Search

We studied atmospheric methane observations from November 2016 to October 2017 from one rural and two urban towers in the Baltimore-Washington region (BWR). Methane observations at these three towers display distinct seasonal and diurnal cycles with maxima at night and in the early morning, reflecting local emissions and boundary layer dynamics. Peaks in winter concentrations and vertical gradients indicate strong local anthropogenic wintertime methane sources in urban regions. In contrast, our analysis shows larger local emissions in summer at the rural site, suggesting a dominant influence of wetland emissions. We compared observed enhancements (mole fractions above the 5th percentile) to simulated methane enhancements using the WRF-STILT model driven by two EDGAR inventories. When run with EDGAR 5.0, the low bias of modeled versus measured methane was greater (ratio of 1.9) than the bias found when using the EDGAR 4.2 emission inventory (ratio of 1.3). However, the correlation of modeled versus measured methane was stronger (~1.2 times higher) for EDGAR 5.0 compared to results found using EDGAR 4.2. In winter, the inclusion of wetland emissions using WETCHARTs had little impact on the mean bias, but during summer, the low bias for all hours using EDGAR 5.0 improved by from 63 to 23 nanomoles per mole of dry air or parts per billion (ppb) at the rural site. We conclude that both versions of EDGAR underestimate the regional anthropogenic emissions of methane, but version 5.0 has a more accurate spatial representation.

Accurate simulation of planetary boundary layer height (PBLH) is key to greenhouse gas emission estimation, air quality prediction, and weather forecasting. This paper describes an extensive performance assessment of several Weather Research and Forecasting (WRF) Model configurations in which novel observations from ceilometers, surface stations, and a flux tower were used to study their ability to reproduce the PBLH and the impact that the urban heat island (UHI) has on the modeled PBLHs in the greater Washington, D.C., area. In addition, CO2 measurements at two urban towers were compared with tracer transport simulations. The ensemble of models used four PBL parameterizations, two sources of initial and boundary conditions, and one configuration including the building energy parameterization urban canopy model. Results have shown low biases over the whole domain and period for wind speed, wind direction, and temperature, with no drastic differences between meteorological drivers. We find that PBLH errors are mostly positively correlated with sensible heat flux errors and that modeled positive UHI intensities are associated with deeper modeled PBLs over the urban areas. In addition, we find that modeled PBLHs are typically biased low during nighttime for most of the configurations with the exception of those using the MYNN parameterization, and these biases directly translate to tracer biases. Overall, the configurations using the MYNN scheme performed the best, reproducing the PBLH and CO2 molar fractions reasonably well during all hours and thus opening the door to future nighttime inverse modeling.

Since greenhouse gas mitigation efforts are mostly being implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO2, CH4, and CO from aircraft over 5 days within an inverse model to estimate emissions from the DC-Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e., 17% for CO2, 24% for CH4, and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

We analyze airborne measurements of atmospheric CO concentration from 70 flights conducted over six years (2015-2020) using an inverse model to quantify the CO emissions from the Washington, DC, and Baltimore metropolitan areas. We found that CO emissions have been declining in the area at a rate of ≈-4.5 % a

Cities are greenhouse gas emission hot spots, making them targets for emission reduction policies. Effective emission reduction policies must be supported by accurate and transparent emissions accounting. Top-down approaches to emissions estimation, based on atmospheric greenhouse gas measurements, are an important and complementary tool to assess, improve, and update the emission inventories on which policy decisions are based and assessed. In this study, we present results from 9 research flights measuring CO2 and CH4 around New York City during the nongrowing seasons of 2018–2020. We used an ensemble of dispersion model runs in a Bayesian inverse modeling framework to derive campaign-average posterior emission estimates for the New York–Newark, NJ, urban area of (125 ± 39) kmol CO2 s–1 and (0.62 ± 0.19) kmol CH4 s–1 (reported as mean ± 1σ variability across the nine flights). We also derived emission estimates of (45 ± 18) kmol CO2 s–1 and (0.20 ± 0.07) kmol CH4 s–1 for the 5 boroughs of New York City. These emission rates, among the first top-down estimates for New York City, are consistent with inventory estimates for CO2 but are 2.4 times larger than the gridded EPA CH4 inventory, consistent with previous work suggesting CH4 emissions from cities throughout the northeast United States are currently underestimated.

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Responses to COVID‐19 have resulted in unintended reductions of city‐scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course‐correct CO2 reduction activities efficiently., Key Points Atmospheric CO2 observations can be used to detect the onset of the COVID‐19 response in Los Angeles and Washington DC/BaltimoreRelative reductions in April 2020 associated with COVID‐19 are ∼30% when compared to emissions in 2018 and 2019Decreases in vehicular traffic do not completely explain observed emissions reductions in both Los Angeles and Washington DC/Baltimore

Increasing atmospheric CO2 measurements in North America, especially in urban areas, may help enable the development of an operational CO2 emission monitoring system. However,...

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state and national level. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop well-targeted emission reductions strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and better inform policy decisions.This presentation builds on previous work estimating methane emissions from New York City and the wider urban area based on measurements taken during nine research flights. We used an ensemble of dispersion model runs in a Bayesian inverse modelling framework to derive posterior emission estimates. Prior emissions were taken from three coarse-resolution inventories based on spatially disaggregated national totals. The most recent version of EDGAR (v5) and the gridded EPA inventory both required upscaling by more than a factor of two to be consistent with our measurements.Here, we construct a high-resolution methane emission prior using a combination of spatial proxies and reported emissions for various sectors. We present preliminary results evaluating the ability of this new prior to represent the magnitude and spatial distribution of emissions, through comparison with both the measured data and results obtained using coarser resolution inventories.

On March 11th , 2020, the World Health Organization (WHO) characterized the COVID-19 respiratory disease caused by the coronavirus (SARS-CoV-2) as a world wide pandemic which led to a massive slowdown in anthropogenic activity as people attempted to "shelter in place". In response to this slowdown NOAA's Global Monitoring Lab (GML), in collaboration with the National Institute of Standards and Technology (NIST), University of Michigan, University of Maryland, Stony Brook University and NOAA's Chemical Science and Atmospheric Resource Laboratories, launched a campaign to measure CO2, CH4, and CO emissions from five major cities along the northeast corridor of the US (Washington, D.C., Baltimore, MD, Philadelphia, PA, New York, NY, and Boston, MA). The month-long campaign which lasted from April 16 to May 16 of 2020 mirrored a campaign that was completed exactly two years prior in April and May of 2018 and which enabled direct comparison of CO2, CH4, CO emissions from these five cities before and during SARS-CoV-2.In this work, we used a Bayesian multi-resolution tiered inversion framework to quantify the CO2, CH4 and CO emissions from these urban areas. We used the HYSPLIT atmospheric transport and dispersion model to calculate the sensitivity of our aircraft observations to surface fluxes (footprints) using three meteorological drivers (NAM, ERA5 and a custom WRF); using three driver models allowed us to account for uncertainties in the transport. To account for biospheric influences on atmospheric CO2, we used a year-specific VPRM simulation that allowed us to isolate the fossil-fuel contribution and solve for it alone. In addition, we also solved for total CO2 and show that not accounting for biogenic activity in lower latitude urban areas could have led to an overestimation of the observed reduction due to biogenic flux differences between the two years.Results show that systematic reductions in CO2 and CO emissions for the five urban areas occurred in April 2020 with signs of recovery in May 2020, which had larger emissions than April 2020. The observed reductions and evolution are consistent with bottom-up estimations based on mobility metrics, which showed the lowest mobility in April with progressive recovery in May. Fuel use from tax records indicates similar reductions. In addition, we show that changes are not homogeneous in space within the urban metropolitan areas and that CO2 and CO emissions reductions are collocated, showing the largest drops in urban centers and roads. While CO2 and CO estimated reductions and evolution are systematic in all cities, CH4 does not show a clear reduction or consistent pattern among cities during the COVID-19 lock-downs. In fact, all the measured changes for CH4 were lower than the standard errors of the differences, implying that the observed changes in CH4 are not significant. Last, we note that since the same prior emissions, constant in time, were used in all the inversions, the anomalous decrease in posterior emissions and subsequent recovery in CO2 and CO observed during the COVID-19 lock-down period are driven by the atmospheric observations and not by temporal changes in the prior emissions.

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. Our analysis uses three lines of evidence with increasing model dependency. The first detects the timing of emissions declines using the variability in atmospheric CO2 observations, the second assesses the continuation of reduced emissions using CO2 enhancements, and the third employs an inverse model to estimate the relative emissions changes in 2020 compared to 2018 and 2019. Emissions declines began in mid-March in both cities. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales, a proxy for vehicular emissions. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines, while the remainder of the emissions reduction remains unexplained. To help diagnose such observed changes in emissions, more reliable, publicly available emission information from all significant sectors needs to be made available. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing urban emissions patterns that can empower cities to course-correct mitigation activities more efficiently.

A credible assessment of a city’s greenhouse gas (GHG) mitigation policies requires a valid account of a city’s emissions. However, questions persist as to whether cities’ ‘self-reported inventories’ (SRIs) are accurate, precise, and consistent enough to track progress toward city mitigation goals. Although useful for broad policy initiatives, city SRIs provide annual snapshots that may have limited use to city managers looking to develop targeted mitigation policies that overlap with other issues like equity, air quality, and human health. An emerging approach from the research community that integrates ‘bottom-up’ hourly, street-level emission data products with ‘top-down’ GHG atmospheric observations have begun to yield production-based (scope 1) GHG estimates that can track changes in emissions at annual and sub-annual timeframes. The use of this integrated approach offers a much-needed assessment of SRIs: the atmospheric observations are tied to international standards and the bottom-up information incorporates multiple overlapping socio-economic data. The emissions are mapped at fine scales which helps link them to attribute information (e.g. fuel types) that can further facilitate mitigation actions. Here, we describe this approach and compare results to the SRI from the City of Indianapolis which shows a yearly difference of 35% in scope 1 emissions. In the City of Baltimore, we show that granular emission information can help address multiple issues, e.g. GHG emissions, air pollution, and inequity, at the sub-zip code scale where many roots and causes for each issue exist. Finally, we show that the incorporation of atmospheric concentrations within an integrated system provides rapid, near-real-time feedback on CO2 emissions anomalies that can uncover important behavioral and economic relationships. An integrated approach to GHG monitoring, reporting and verification can ensure uniformity, and provide accuracy to city-scale GHG emissions, scalable to states and the nation—ultimately helping cities meet stated ambitions.

Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO(2) emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012–2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO(2) emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO(2) boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO(2) emissions based on the ensemble mean and quartiles (5.26–5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

In the past decade, there has been a scientific focus on improving the accuracy and precision of methane (CH4) emission estimates in the United States, with much effort targeting oil and natural gas producing basins. Yet, regional CH4 emissions and their attribution to specific sources continue to have significant associated uncertainties. Recent urban work using aircraft observations have suggested that CH4 emissions are not well characterized in major cities along the U.S. East Coast; discrepancies have been attributed to an under-estimation of fugitive emissions from the distribution of natural gas. However, much of regional and urban research has involved the use of aircraft campaigns that can only provide a spatio-temporal snapshot of the CH4 emission landscape. As such, the annual representation and the seasonal variability of emissions remain largely unknown. To further investigate CH4 emissions, we present preliminary CH4 emissions estimates in the Northeastern US as part of NIST’s Northeast Corridor (NEC) testbed project using a regional inversion framework. This area encompasses over 20% of the US and contains many of the dominant CH4 emissions sources important at both regional and local scales. The atmospheric inversion can estimate sub-monthly 0.1-degree emissions using observations from a regional network of up to 37 in-situ towers; some towers are in non-urban areas while others are in cities or suburban areas. The inversion uses different emission products to help provide a prior constraint within the inversion including anthropogenic emissions from both the EDGAR v42 for the year 2008 and the US EPA for the year 2012, and natural wetland CH4 emissions from the WetCHARTs ensemble mean for the year 2010. Results include the comparison of synthetic model simulated CH4 concentrations (i.e., convolutions of the emission products with WRF-STILT footprints + background) to mole-fractions measured at the regional in-situ sites. The comparison provides an indication as to how well our prior understanding of emissions and incoming air flow matches the atmospheric signatures due to the underlying CH4 sources. We also present a preliminary set of CH4 fluxes for a selected number of urban centers and discuss challenges estimating highly-resolved methane emissions using high-frequency in-situ observations for a regional domain (e.g. few constraints, skewness in underlying fluxes, representing incoming background, etc.). Overall, this work provides the basis for a year-long inversion that will yields regional CH4 emissions over the Northeast US with a focus on Eastern urban areas.

Since greenhouse gas mitigation efforts are being mostly implemented in cities, the ability to quantify emission trends for urban environments is of paramount importance. However, previous aircraft work has indicated large daily variability in the results. Here we use measurements of CO(2), CH(4) and CO from aircraft over five days within an inverse model to estimate emissions from the D.C./Baltimore region. Results show good agreement with previous estimates in the area for all three gases. However, aliasing caused by irregular spatiotemporal sampling of emissions is shown to significantly impact both the emissions estimates and their variability. Extensive sensitivity tests allow us to quantify the contributions of different sources of variability and indicate that daily variability in posterior emissions estimates is larger than the uncertainty attributed to the method itself (i.e. 17% for CO(2), 24% for CH(4) and 13% for CO). Analysis of hourly reported emissions from power plants and traffic counts shows that 97% of the daily variability in posterior emissions estimates is explained by accounting for the sampling in time and space of sources that have large hourly variability and, thus, caution must be taken in properly interpreting variability that is caused by irregular spatiotemporal sampling conditions.

Urban areas contribute approximately three-quarters of fossil fuel derived CO2 emissions, and many cities have enacted emissions mitigation plans. Evaluation of the effectiveness of mitigation efforts will require measurement of both the emission rate and its change over space and time. The relative performance of different emission estimation methods is a critical requirement to support mitigation efforts. Here we compare results of CO2 emissions estimation methods including an inventory-based method and two different top-down atmospheric measurement approaches implemented for the Indianapolis, Indiana, U.S.A. urban area in winter. By accounting for differences in spatial and temporal coverage, as well as trace gas species measured, we find agreement among the wintertime whole-city fossil fuel CO2 emission rate estimates to within 7%. This finding represents a major improvement over previous comparisons of urban-scale emissions, making urban CO2 flux estimates from this study consistent with local and global emission mitigation strategy needs. The complementary application of multiple scientifically driven emissions quantification methods enables and establishes this high level of confidence and demonstrates the strength of the joint implementation of rigorous inventory and atmospheric emissions monitoring approaches.

Advances in natural gas extraction technology have led to increased activity in the production and transport sectors in the United States and, as a consequence, an increased need for reliable monitoring of methane leaks to the atmosphere. We present a statistical methodology in combination with an observing system for the detection and attribution of fugitive emissions of methane from distributed potential source location landscapes such as natural gas production sites. We measure long (> 500 m), integrated open-path concentrations of atmospheric methane using a dual frequency comb spectrometer and combine measurements with an atmospheric transport model to infer leak locations and strengths using a novel statistical method, the non-zero minimum bootstrap (NZMB). The new statistical method allows us to determine whether the empirical distribution of possible source strengths for a given location excludes zero. Using this information, we identify leaking source locations (i.e., natural gas wells) through rejection of the null hypothesis that the source is not leaking. The method is tested with a series of synthetic data inversions with varying measurement density and varying levels of model–data mismatch. It is also tested with field observations of (1) a non-leaking source location and (2) a source location where a controlled emission of 3.1 × 10−5 kg s−1 of methane gas is released over a period of several hours. This series of synthetic data tests and outdoor field observations using a controlled methane release demonstrates the viability of the approach for the detection and sizing of very small leaks of methane across large distances (4+ km2 in synthetic tests). The field tests demonstrate the ability to attribute small atmospheric enhancements of 17 ppb to the emitting source location against a background of combined atmospheric (e.g., background methane variability) and measurement uncertainty of 5 ppb (1σ), when measurements are averaged over 2 min. The results of the synthetic and field data testing show that the new observing system and statistical approach greatly decreases the incidence of false alarms (that is, wrongly identifying a well site to be leaking) compared with the same tests that do not use the NZMB approach and therefore offers increased leak detection and sizing capabilities.

Natural gas infrastructure releases methane (CH4), a potent greenhouse gas, into the atmosphere. The estimated emission rate associated with the production and transportation of natural gas is uncertain, hindering our understanding of its greenhouse footprint. This study presents a new application of inverse methodology for estimating regional emission rates from natural gas production and gathering facilities in north-eastern Pennsylvania. An inventory of CH4 emissions was compiled for major sources in Pennsylvania. This inventory served as input emission data for the Weather Research and Forecasting model with chemistry enabled (WRF-Chem), and atmospheric CH4 mole fraction fields were generated at 3 km resolution. Simulated atmospheric CH4 enhancements from WRF-Chem were compared to observations obtained from a 3-week flight campaign in May 2015. Modelled enhancements from sources not associated with upstream natural gas processes were assumed constant and known and therefore removed from the optimization procedure, creating a set of observed enhancements from natural gas only. Simulated emission rates from unconventional production were then adjusted to minimize the mismatch between aircraft observations and model-simulated mole fractions for 10 flights. To evaluate the method, an aircraft mass balance calculation was performed for four flights where conditions permitted its use. Using the model optimization approach, the weighted mean emission rate from unconventional natural gas production and gathering facilities in north-eastern Pennsylvania approach is found to be 0.36 % of total gas production, with a 2σ confidence interval between 0.27 and 0.45 % of production. Similarly, the mean emission estimates using the aircraft mass balance approach are calculated to be 0.40 % of regional natural gas production, with a 2σ confidence interval between 0.08 and 0.72 % of production. These emission rates as a percent of production are lower than rates found in any other basin using a top-down methodology, and may be indicative of some characteristics of the basin that make sources from the north-eastern Marcellus region unique.

Non-dispersive infrared (NDIR) sensors are a low-cost way to observe carbon dioxide concentrations in air, but their specified accuracy and precision are not sufficient for some scientific applications. An initial evaluation of six SenseAir K30 carbon dioxide NDIR sensors in a lab setting showed that without any calibration or correction, the sensors have an individual root mean square error (RMSE) between ∼ 5 and 21 parts per million (ppm) compared to a research-grade greenhouse gas analyzer using cavity enhanced laser absorption spectroscopy. Through further evaluation, after correcting for environmental variables with coefficients determined through a multivariate linear regression analysis, the calculated difference between the each of six individual K30 NDIR sensors and the higher-precision instrument had an RMSE of between 1.7 and 4.3 ppm for 1 min data. The median RMSE improved from 9.6 for off-the-shelf sensors to 1.9 ppm after correction and calibration, demonstrating the potential to provide useful information for ambient air monitoring.

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in-situ carbon dioxide and methane observations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the north-eastern United States. A specific focus of the network is the cities of Baltimore, Maryland, and Washington, D.C., USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the US northeast, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Urban areas are increasingly recognized as an important source of methane (CH4), but we have limited seasonally resolved observations of these regions. In this study, we quantify seasonal and annual urban CH4 emissions over the Baltimore, Maryland, and Washington, DC metropolitan regions. We use CH4 atmospheric observations from four tall tower stations and a Lagrangian particle dispersion model to simulate CH4 concentrations at these stations. We directly compare these simulations with observations and use a geostatistical inversion method to determine optimal emissions to match our observations. We use observations spanning four seasons and employ an ensemble approach considering multiple meteorological representations, emission inventories, and upwind CH4 values. Forward simulations in winter, spring, and fall underestimate observed atmospheric CH4 while in summer, simulations overestimate observations because of excess modeled wetland emissions. With ensemble geostatistical inversions, the optimized annual emissions in DC/Baltimore are 39 ± 9 Gg/month (1 δ), 2.0 ± 0.4 times higher than the ensemble mean of bottom-up emission inventories. We find a modest seasonal variability of urban CH4 emissions not captured in current inventories, with optimized summer emissions ∼41% lower than winter, broadly consistent with expectations if emissions are dominated by fugitive natural gas sources that correlate with natural gas usage.

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.

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.

We present in situ airborne measurements of methane (CH4) and ethane (C2H6) taken aboard a NOAA DHC-6 Twin Otter research aircraft in May 2014 over the Williston Basin in northwestern North Dakota, a region of rapidly growing oil and natural gas production. The Williston Basin is best known for the Bakken shale formation, from which a significant increase in oil and gas extraction has occurred since 2009. We derive a CH4 emission rate from this region using airborne data by calculating the CH4 enhancement flux through the planetary boundary layer downwind of the region. We calculate CH4 emissions of (36 ± 13), (27 ± 13), (27 ± 12), (27 ± 12), and (25 ± 10) × 103 kg/h from five transects on 3 days in May 2014 downwind of the Bakken shale region of North Dakota. The average emission, (28 ± 5) × 103 kg/h, extrapolates to 0.25 ± 0.05 Tg/yr, which is significantly lower than a previous estimate of CH4 emissions from northwestern North Dakota and southeastern Saskatchewan using satellite remote sensing data. We attribute the majority of CH4 emissions in the region to oil and gas operations in the Bakken based on the similarity between atmospheric C2H6 to CH4 enhancement ratios and the composition of raw natural gas withdrawn from the region.

Urban areas contribute approximately three-quarters of fossil fuel derived CO

Greenhouse gas emissions mitigation requires understanding dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emissions sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, U.S.A. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted downwind methane concentrations given the same emissions field. The dispersion models tested often under-predicted the observed methane enhancements with significant variability between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

We present estimates of regional methane (CH4) emissions from oil and natural gas operations in the Barnett Shale, Texas, using airborne atmospheric measurements. Using a mass balance approach on eight different flight days in March and October 2013, the total CH4 emissions for the region are estimated to be 76 ± 13 × 10(3) kg hr(-1) (equivalent to 0.66 ± 0.11 Tg CH4 yr(-1); 95% confidence interval (CI)). We estimate that 60 ± 11 × 10(3) kg CH4 hr(-1) (95% CI) are emitted by natural gas and oil operations, including production, processing, and distribution in the urban areas of Dallas and Fort Worth. This estimate agrees with the U.S. Environmental Protection Agency (EPA) estimate for nationwide CH4 emissions from the natural gas sector when scaled by natural gas production, but it is higher than emissions reported by the EDGAR inventory or by industry to EPA's Greenhouse Gas Reporting Program. This study is the first to show consistency between mass balance results on so many different days and in two different seasons, enabling better quantification of the related uncertainty. The Barnett is one of the largest production basins in the United States, with 8% of total U.S. natural gas production, and thus, our results represent a crucial step toward determining the greenhouse gas footprint of U.S. onshore natural gas production.

We present high time resolution airborne ethane (C2H6) and methane (CH4) measurements made in March and October 2013 as part of the Barnett Coordinated Campaign over the Barnett Shale formation in Texas. Ethane fluxes are quantified using a downwind flight strategy, a first demonstration of this approach for C2H6. Additionally, ethane-to-methane emissions ratios (C2H6:CH4) of point sources were observationally determined from simultaneous airborne C2H6 and CH4 measurements during a survey flight over the source region. Distinct C2H6:CH4 × 100% molar ratios of 0.0%, 1.8%, and 9.6%, indicative of microbial, low-C2H6 fossil, and high-C2H6 fossil sources, respectively, emerged in observations over the emissions source region of the Barnett Shale. Ethane-to-methane correlations were used in conjunction with C2H6 and CH4 fluxes to quantify the fraction of CH4 emissions derived from fossil and microbial sources. On the basis of two analyses, we find 71-85% of the observed methane emissions quantified in the Barnett Shale are derived from fossil sources. The average ethane flux observed from the studied region of the Barnett Shale was 6.6 ± 0.2 × 10(3) kg hr(-1) and consistent across six days in spring and fall of 2013.

Seasonal spatial and temporal gradients for the CO2 mole fraction over North America are examined by creating a climatology from data collected 2004–2013 by the NOAA/ESRL Global Greenhouse Gas Reference Network Aircraft Program relative to trends observed for CO2 at the Mauna Loa Observatory. The data analyzed are from measurements of air samples collected in specially fabricated flask packages at frequencies of days to months at 22 sites over continental North America and shipped back to Boulder, Colorado, for analysis. These measurements are calibrated relative to the CO2 World Meteorological Organization mole fraction scale. The climatologies of CO2 are compared to climatologies of CO, CH4, SF6, N2O (which are also measured from this sampling program), and winds to understand the dominant transport and chemical and biological processes driving changes in the spatial and temporal mole fractions of CO2 as air passes over continental North America. The measurements show that air masses coming off the Pacific on the west coast of North America are relatively homogeneous with altitude. As air masses flow eastward, the lower section from the surface to 4000 m above sea level (masl) becomes distinctly different from the 4000–8000 masl section of the column. This is due in part to the extent of the planetary boundary layer, which is directly impacted by continental sources and sinks, and to the vertical gradient in west-to-east wind speeds. The slowdown and southerly shift in winds at most sites during summer months amplify the summertime drawdown relative to what might be expected from local fluxes. This influence counteracts the dilution of summer time CO2 drawdown (known as the “rectifier effect”) as well as changes the surface influence “footprint” for each site. An early start to the summertime drawdown, a pronounced seasonal cycle in the column mean (500 to 8000 masl), and small vertical gradients in CO2, CO, CH4, SF6, and N2O at high-latitude western sites such as Poker Flat, Alaska, suggest recent influence of transport from southern latitudes and not local processes. This transport pathway provides a significant contribution to the large seasonal cycle observed in the high latitudes at all altitudes sampled. A sampling analysis of the NOAA/ESRL CarbonTracker model suggests that the average sampling resolution of 22 days is sufficient to get a robust estimate of mean seasonal cycle of CO2 during this 10 year period but insufficient to detect interannual variability in emissions over North America.

This paper describes the atmospheric modeling that underlies the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) science analysis, including its meteorological and atmospheric transport components (Polar variant of the Weather Research and Forecasting (WRF) and Stochastic Time Inverted Lagrangian Transport (STILT) models), and provides WRF validation for May–October 2012 and March–November 2013 – the first two years of the aircraft field campaign. A triply nested computational domain for WRF was chosen so that the innermost domain with 3.3 km grid spacing encompasses the entire mainland of Alaska and enables the substantial orography of the state to be represented by the underlying high-resolution topographic input field. Summary statistics of the WRF model performance on the 3.3 km grid indicate good overall agreement with quality-controlled surface and radiosonde observations. Two-meter temperatures are generally too cold by approximately 1.4 K in 2012 and 1.1 K in 2013, while 2 m dewpoint temperatures are too low (dry) by 0.2 K in 2012 and too high (moist) by 0.6 K in 2013. Wind speeds are biased too low by 0.2 m s−1 in 2012 and 0.3 m s−1 in 2013. Model representation of upper level variables is very good. These measures are comparable to model performance metrics of similar model configurations found in the literature. The high quality of these fine-resolution WRF meteorological fields inspires confidence in their use to drive STILT for the purpose of computing surface influences ("footprints") at commensurably increased resolution. Indeed, footprints generated on a 0.1° grid show increased spatial detail compared with those on the more common 0.5° grid, lending itself better for convolution with flux models for carbon dioxide and methane across the heterogeneous Alaskan landscape. Ozone deposition rates computed using STILT footprints indicate good agreement with observations and exhibit realistic seasonal variability, further indicating that WRF-STILT footprints are of high quality and will support accurate estimates of CO2 and CH4 surface–atmosphere fluxes using CARVE observations.

The Indianapolis Flux Experiment (INFLUX) aims to develop and assess methods for quantifying urban greenhouse gas emissions. Here we use CO2, 14CO2, and CO measurements from tall towers around Indianapolis, USA, to determine urban total CO2, the fossil fuel derived CO2 component (CO2ff), and CO enhancements relative to background measurements. When a local background directly upwind of the urban area is used, the wintertime total CO2 enhancement over Indianapolis can be entirely explained by urban CO2ff emissions. Conversely, when a continental background is used, CO2ff enhancements are larger and account for only half the total CO2 enhancement, effectively representing the combined CO2ff enhancement from Indianapolis and the wider region. In summer, we find that diurnal variability in both background CO2 mole fraction and covarying vertical mixing makes it difficult to use a simple upwind-downwind difference for a reliable determination of total CO2 urban enhancement. We use characteristic CO2ff source sector CO:CO2ff emission ratios to examine the contribution of the CO2ff source sectors to total CO2ff emissions. This method is strongly sensitive to the mobile sector, which produces most CO. We show that the inventory-based emission product (“bottom up”) and atmospheric observations (“top down”) can be directly compared throughout the diurnal cycle using this ratio method. For Indianapolis, the top-down observations are consistent with the bottom-up Hestia data product emission sector patterns for most of the diurnal cycle but disagree during the nighttime hours. Further examination of both the top-down and bottom-up assumptions is needed to assess the exact cause of the discrepancy.

Recent increases in oil and natural gas (NG) production throughout the western US have come with scientific and public interest in emission rates, air quality and climate impacts related to this industry. This study uses a regional scale air quality model WRF-Chem to simulate high ozone (O3) episodes during the winter of 2013 over the Uinta Basin (UB) in northeastern Utah, which is densely populated by thousands of oil and NG wells. The high resolution meteorological simulations are able to qualitatively reproduce the wintertime cold pool conditions that occurred in 2013, allowing the model to reproduce the observed multi-day buildup of atmospheric pollutants and accompanying rapid photochemical ozone formation in the UB. Two different emission scenarios for the oil and NG sector were employed in this study. The first emission scenario (bottom-up) was based on the US EPA National Emission Inventory (NEI) (2011, version 1) for the oil and NG sector for the UB. The second emission scenario (top-down) was based on the previously derived estimates of methane (CH4) emissions and a regression analysis for multiple species relative to CH4 concentration measurements in the UB. WRF-Chem simulations using the two emission data sets resulted in significant differences for concentrations of most gas-phase species. Evaluation of the model results shows greater underestimates of CH4 and other volatile organic compounds (VOCs) in the simulation with the NEI-2011 inventory than the case when the top-down emission scenario was used. Unlike VOCs, the NEI-2011 inventory significantly overestimates the emissions of nitrogen oxides (NOx), while the top-down emission scenario results in a moderate negative bias. Comparison of simulations using the two emission data sets reveals that the top-down case captures the high O3 episodes. In contrast, the simulation case using the bottom-up inventory is not able to reproduce any of the observed high O3 concentrations in the UB. A sensitivity analysis reveals that the major factors driving high wintertime O3 in the UB are shallow boundary layers with light winds, high emissions of VOCs from oil and NG operations compared to NOx emissions, enhancement of photolysis fluxes and reduction of O3 loss from deposition due to snow cover. Simple emission reduction scenarios show that the UB O3 production is VOC sensitive and NOx insensitive. The model results show a disproportionate contribution of aromatic VOCs to O3 formation relative to all other VOC emissions. We also present modeling results for winter of 2012, when high O3 levels were not observed in the UB. The air quality model together with the top-down emission framework presented here may help to address the emerging science and policy related questions surrounding the environmental impact of oil and NG drilling in western US.

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.