SLIDES: What We Know (and Don\u27t Know) About Air Quality Impacts of Oil and Gas Development

Presenter: Anna Karion, NOAA/ESRL, University of Colorado/CIRES 35 slide

SLIDES: What We Know (and Don\u27t Know) About Air Quality Impacts of Oil and Gas Development

Presenter: Anna Karion, NOAA/ESRL, University of Colorado/CIRES 35 slide

Likelihood informed dimension reduction for inverse problems in remote sensing of atmospheric constituent profiles

We use likelihood informed dimension reduction (LIS) (T. Cui et al. 2014) for inverting vertical profile information of atmospheric methane from ground based Fourier transform infrared (FTIR) measurements at Sodankyl\"a, Northern Finland. The measurements belong to the word wide TCCON network for greenhouse gas measurements and, in addition to providing accurate greenhouse gas measurements, they are important for validating satellite observations. LIS allows construction of an efficient Markov chain Monte Carlo sampling algorithm that explores only a reduced dimensional space but still produces a good approximation of the original full dimensional Bayesian posterior distribution. This in effect makes the statistical estimation problem independent of the discretization of the inverse problem. In addition, we compare LIS to a dimension reduction method based on prior covariance matrix truncation used earlier (S. Tukiainen et al. 2016)

Siting Background Towers to Characterize Incoming Air for Urban Greenhouse Gas Estimation: A Case Study in the Washington, DC/Baltimore Area

There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridorâ Baltimore/Washington, DC (NECâ B/W), downwind of large fluxes. To help site background towers for the NECâ B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, rootâ meanâ square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.Key PointsFactoring in the variability of greenhouse gas enhancements in incoming air is critical for estimating emissions in an urban domainStatistical methods were used to site four towers sampling background air in the Washington, DC/Baltimore regionOptimal background tower configurations for representing incoming air can still have large errors for any given urban GHG observationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/1/jgrd54353_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142902/2/jgrd54353.pd

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations)

Signatures of granular microstructure in dense shear flows

Granular materials react to shear stresses differently than do ordinary fluids. Rather than deforming uniformly, materials such as dry sand or cohesionless powders develop shear bands: narrow zones containing large relative particle motion leaving adjacent regions essentially rigid[1,2,3,4,5]. Since shear bands mark areas of flow, material failure and energy dissipation, they play a crucial role for many industrial, civil engineering and geophysical processes[6]. They also appear in related contexts, such as in lubricating fluids confined to ultra-thin molecular layers[7]. Detailed information on motion within a shear band in a three-dimensional geometry, including the degree of particle rotation and inter-particle slip, is lacking. Similarly, only little is known about how properties of the individual grains - their microstructure - affect movement in densely packed material[5]. Combining magnetic resonance imaging, x-ray tomography, and high-speed video particle tracking, we obtain the local steady-state particle velocity, rotation and packing density for shear flow in a three-dimensional Couette geometry. We find that key characteristics of the granular microstructure determine the shape of the velocity profile.Comment: 5 pages, incl. 4 figure

Quantifying atmospheric methane emissions from oil and natural gas production in the Bakken shale region of North Dakota

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.Key PointsCH4 emissions from the Bakken region of North Dakota quantifiedFirst emission estimate using in situ CH4 measurementsCH4 sources dominated by oil‐ and gas‐related activitiesPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/122415/1/jgrd52986.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/122415/2/jgrd52986_am.pd

Assessment of fossil fuel carbon dioxide and other anthropogenic trace gas emissions from airborne measurements over Sacramento, California in spring 2009

Direct quantification of fossil fuel CO<sub>2</sub> (CO<sub>2</sub>ff) in atmospheric samples can be used to examine several carbon cycle and air quality questions. We collected in situ CO<sub>2</sub>, CO, and CH<sub>4</sub> measurements and flask samples in the boundary layer and free troposphere over Sacramento, California, USA, during two aircraft flights over and downwind of this urban area during spring of 2009. The flask samples were analyzed for Δ<sup>14</sup>CO<sub>2</sub> and CO<sub>2</sub> to determine the recently added CO<sub>2</sub>ff mole fraction. A suite of greenhouse and other trace gases, including hydrocarbons and halocarbons, were measured in the same samples. Strong correlations were observed between CO<sub>2</sub>ff and numerous trace gases associated with urban emissions. From these correlations we estimate emission ratios between CO<sub>2</sub>ff and these species, and compare these with bottom-up inventory-derived estimates. Recent county level inventory estimates for carbon monoxide (CO) and benzene from the California Air Resources Board CEPAM database are in good agreement with our measured emission ratios, whereas older emissions inventories appear to overestimate emissions of these gases by a factor of two. For most other trace species, there are substantial differences (200–500%) between our measured emission ratios and those derived from available emission inventories. For the first flight, we combine in situ CO measurements with the measured CO:CO<sub>2</sub>ff emission ratio of 14 ± 2 ppbCO/ppmCO<sub>2</sub> to derive an estimate of CO<sub>2</sub>ff mole fraction throughout this flight, and also estimate the biospheric CO<sub>2</sub> mixing ratio (CO<sub>2</sub>bio) from the difference of total and fossil CO<sub>2</sub>. The resulting CO<sub>2</sub>bio varies dramatically from up to 8 ± 2 ppm in the urban plume to −6 ± 1 ppm in the surrounding boundary layer air. Finally, we use the in situ estimates of CO<sub>2</sub>ff mole fraction to infer total fossil fuel CO<sub>2</sub> emissions from the Sacramento region, using a mass balance approach. The resulting emissions are uncertain to within a factor of two due to uncertainties in wind speed and boundary layer height. Nevertheless, this first attempt to estimate urban-scale CO<sub>2</sub>ff from atmospheric radiocarbon measurements shows that CO<sub>2</sub>ff can be used to verify and improve emission inventories for many poorly known anthropogenic species, separate biospheric CO<sub>2</sub>, and indicates the potential to constrain CO<sub>2</sub>ff emissions if transport uncertainties are reduced

Carbon Dioxide Sources from Alaska Driven by Increasing Early Winter Respiration from Artic Tundra

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO2) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO2 with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO2 fluxes across Alaska during 2012-2014. We find that tundra ecosystems were a net source of CO2 to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO2emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO2in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO2 seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate

Long-term greenhouse gas measurements from aircraft

In March 2009 the NOAA/ESRL/GMD Carbon Cycle and Greenhouse Gases Group collaborated with the US Coast Guard (USCG) to establish the Alaska Coast Guard (ACG) sampling site, a unique addition to NOAA's atmospheric monitoring network. This collaboration takes advantage of USCG bi-weekly Arctic Domain Awareness (ADA) flights, conducted with Hercules C-130 aircraft from March to November each year. Flights typically last 8 h and cover a large area, traveling from Kodiak up to Barrow, Alaska, with altitude profiles near the coast and in the interior. NOAA instrumentation on each flight includes a flask sampling system, a continuous cavity ring-down spectroscopy (CRDS) carbon dioxide (CO2)/methane (CH4)/carbon monoxide (CO)/water vapor (H2O) analyzer, a continuous ozone analyzer, and an ambient temperature and humidity sensor. Air samples collected in flight are analyzed at NOAA/ESRL for the major greenhouse gases and a variety of halocarbons and hydrocarbons that influence climate, stratospheric ozone, and air quality. We describe the overall system for making accurate greenhouse gas measurements using a CRDS analyzer on an aircraft with minimal operator interaction and present an assessment of analyzer performance over a three-year period. Overall analytical uncertainty of CRDS measurements in 2011 is estimated to be 0.15 ppm, 1.4 ppb, and 5 ppb for CO2, CH4, and CO, respectively, considering short-term precision, calibration uncertainties, and water vapor correction uncertainty. The stability of the CRDS analyzer over a seven-month deployment period is better than 0.15 ppm, 2 ppb, and 4 ppb for CO2, CH4, and CO, respectively, based on differences of on-board reference tank measurements from a laboratory calibration performed prior to deployment. This stability is not affected by variation in pressure or temperature during flight. We conclude that the uncertainty reported for our measurements would not be significantly affected if the measurements were made without in-flight calibrations, provided ground calibrations and testing were performed regularly. Comparisons between in situ CRDS measurements and flask measurements are consistent with expected measurement uncertainties for CH4 and CO, but differences are larger than expected for CO2. Biases and standard deviations of comparisons with flask samples suggest that atmospheric variability, flask-to-flask variability, and possible flask sampling biases may be driving the observed flask versus in situ CO2 differences rather than the CRDS measurements