Aerosol lidar observations of atmospheric mixing in Los Angeles: Climatology and implications for greenhouse gas observations

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport—especially vertical mixing—is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day‐to‐day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL’s portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing.Key PointsAerosol lidar maps LA mixing depth in space (pilot mobile study) and time (2 years data)Automatic mixing depth retrieval system finds daily variability far exceeds seasonal differencePBL heights in models used for GHG monitoring show biases that will carry over to flux estimatesPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134180/1/jgrd53200_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134180/2/jgrd53200.pd

Space-based observations of megacity carbon dioxide

Urban areas now house more than half the world's population, and are estimated to contribute over 70% of global energy-related CO_2 emissions. Many cities have emission reduction policies in place, but lack objective, observation-based methods for verifying their outcomes. Here we demonstrate the potential of satellite-borne instruments to provide accurate global monitoring of megacity CO_2 emissions using GOSAT observations of column averaged CO_2 dry air mole fraction (X_(CO_2)) collected over Los Angeles and Mumbai. By differencing observations over the megacity with those in nearby background, we observe robust, statistically significant X_(CO_2) enhancements of 3.2 ± 1.5 ppm for Los Angeles and 2.4 ± 1.2 ppm for Mumbai, and find these enhancements can be exploited to track anthropogenic emission trends over time. We estimate that X_(CO_2) changes as small as 0.7 ppm in Los Angeles, corresponding to a 22% change in emissions, could be detected with GOSAT at the 95% confidence level

Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis

Background: Gene set enrichment analysis (GSEA) is an analytic approach which simultaneously reduces the dimensionality of microarray data and enables ready inference of the biological meaning of observed gene expression patterns. Here we invert the GSEA process to identify class-specific gene signatures. Because our approach uses the Kolmogorov-Smirnov approach both to define class specific signatures and to classify samples using those signatures, we have termed this methodology “Dual-KS” (DKS). Results: The optimum gene signature identified by the DKS algorithm was smaller than other methods to which it was compared in 5 out of 10 datasets. The estimated error rate of DKS using the optimum gene signature was smaller than the estimated error rate of the random forest method in 4 out of the 10 datasets, and was equivalent in two additional datasets. DKS performance relative to other benchmarked algorithms was similar to its performance relative to random forests. Conclusions: DKS is an efficient analytic methodology that can identify highly parsimonious gene signatures useful for classification in the context of microarray studies. The algorithm is available as the dualKS package for R as part of the bioconductor project

Large Fugitive Methane Emissions From Urban Centers Along the U.S. East Coast

Urban emissions remain an underexamined part of the methane budget. Here we present and interpret aircraft observations of six old and leak‐prone major cities along the East Coast of the United States. We use direct observations of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), ethane (C2H6), and their correlations to quantify CH4 emissions and attribute to natural gas. We find the five largest cities emit 0.85 (0.63, 1.12) Tg CH4/year, of which 0.75 (0.49, 1.10) Tg CH4/year is attributed to natural gas. Our estimates, which include all thermogenic methane sources including end use, are more than twice that reported in the most recent gridded EPA inventory, which does not include end‐use emissions. These results highlight that current urban inventory estimates of natural gas emissions are substantially low, either due to underestimates of leakage, lack of inclusion of end‐use emissions, or some combination thereof.Plain Language SummaryRecent efforts to quantify fugitive methane associated with the oil and gas sector, with a particular focus on production, have resulted in significant revisions upward of emission estimates. In comparison, however, there has been limited focus on urban methane emissions. Given the volume of gas distributed and used in cities, urban losses can impact national‐level emissions. In this study we use aircraft observations of methane, carbon dioxide, carbon monoxide, and ethane to determine characteristic correlation slopes, enabling quantification of urban methane emissions and attribution to natural gas. We sample nearly 12% of the U.S. population and 4 of the 10 most populous cities, focusing on older, leak‐prone urban centers. Emission estimates are more than twice the total in the U.S. EPA inventory for these regions and are predominantly attributed to fugitive natural gas losses. Current estimates for methane emissions from the natural gas supply chain appear to require revision upward, in part possibly by including end‐use emissions, to account for these urban losses.Key PointsAircraft observations downwind of six major cities along the U.S. East Coast are used to estimate urban methane emissionsObserved urban methane estimates are about twice that reported in the Gridded EPA inventoryMethane emissions from natural gas (including end use) in five cities combined exceeds nationwide emissions estimate from local distributionPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151283/1/grl59329.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151283/2/grl59329_am.pd

Detecting Urban Emissions Changes and Events With a Near‐Real‐Time‐Capable Inversion System

In situ observing networks are increasingly being used to study greenhouse gas emissions in urban environments. While the need for sufficiently dense observations has often been discussed, density requirements depend on the question posed and interact with other choices made in the analysis. Focusing on the interaction of network density with varied meteorological information used to drive atmospheric transport, we perform geostatistical inversions of methane flux in the South Coast Air Basin, California, in 2015–2016 using transport driven by a locally tuned Weather Research and Forecasting configuration as well as by operationally available meteorological products. We find total‐basin flux estimates vary by as much as a factor of two between inversions, but the spread can be greatly reduced by calibrating the estimates to account for modeled sensitivity. Using observations from the full Los Angeles Megacities Carbon Project observing network, inversions driven by low‐resolution generic wind fields are robustly sensitive (p < 0.05) to seasonal differences in methane flux and to the increase in emissions caused by the 2015 Aliso Canyon natural gas leak. When the number of observing sites is reduced, the basin‐wide sensitivity degrades, but flux events can be detected by testing for changes in flux variance, and even a single site can robustly detect basin‐wide seasonal flux variations. Overall, an urban monitoring system using an operational methane observing network and off‐the‐shelf meteorology could detect many seasonal or event‐driven changes in near real time—and, if calibrated to a model chosen as a transfer standard, could also quantify absolute emissions.Key PointsLA CH4 flux estimates differ by driving meteorology but agree when calibrated for model sensitivityAliso Canyon leak can be detected by inversions using operational meteorologyOperational meteorology driven inversions significantly detect seasonal emission changes even with only one sitePeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149534/1/jgrd55279-sup-0001-Text_SI-S01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149534/2/jgrd55279.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149534/3/jgrd55279_am.pd

Regional Methane Emission Estimation Based on Observed Atmospheric Concentrations (2002-2012)

Methane (CH4) plays important roles in atmospheric chemistry and short-term forcing of climate. A clear understanding of atmospheric CH4’s budget of emissions and losses is required to aid sustainable management of Earth’s future environment. We used an atmospheric chemistry-transport model (JAMSTEC’s ACTM) for simulating atmospheric CH4. A global inverse modeling system has been developed for estimating CH4 emissions from 53 land regions for 2002-2012 using measurements at 39 sites. An ensemble of 7 inversions is performed by varying a priori emissions. Global net CH4 emissions varied between 505-509 and 524-545 Tg yr-1 during 2002-2006 and 2008-2012, respectively (ranges based on 7 inversion cases), with a step like increase in 2007 in agreement with atmospheric measurements. The inversion system did not account for interannual variations in OH radicals reacting with CH4 in the atmosphere. Our results suggest that the recent update of the EDGAR inventory (version 4.2FT2010) overestimated the global total emissions by at least 25 Tg yr-1 in 2010. The increase in CH4 emission since 2004 originated in the tropical and southern hemisphere regions, coinciding with an increase in non-dairy cattle stocks by ~10 % from 2002 (with 1056 million heads) to 2012, leading to ~10 Tg yr-1 increase in emissions from enteric fermentation. All 7 ensemble cases robustly estimated the interannual variations in emissions, but poorly constrained the seasonal cycle amplitude or phase consistently for all regions due to the sparse observational network. Forward simulation results using both a priori and a posteriori emissions are compared with independent aircraft measurements for validation. Based on the results of the comparison, we reject the upper limit (545 Tg yr-1) of global total emissions as 14 Tg yr-1 too high during 2008-2012, which allows us to further conclude that the increase in CH4 emissions over the East Asia (mainly China) region was 7-8 Tg yr-1 between the 2002-2006 and 2008-2012 periods, contrary to 1-17 Tg yr-1 in the a priori emissions

Constraining Aerosol Vertical Profile in the Boundary Layer Using Hyperspectral Measurements of Oxygen Absorption

This study attempts to infer aerosol vertical structure in the urban boundary layer using passive hyperspectral measurements. A spectral sorting technique is developed to retrieve total aerosol optical depth (AOD) and effective aerosol layer height (ALH) from hyperspectral measurements in the 1.27‐μm oxygen absorption band by the mountaintop Fourier Transform Spectrometer at the California Laboratory for Atmospheric Remote Sensing instrument (1,673 m above sea level) overlooking the LA basin. Comparison to AOD measurements from Aerosol Robotic Network and aerosol backscatter profile measurements from a Mini MicroPulse Lidar shows agreement, with coefficients of determination (r^2) of 0.74 for AOD and 0.57 for effective ALH. On average, the AOD retrieval has an error of 24.9% and root‐mean‐square error of 0.013, while the effective ALH retrieval has an error of 7.8% and root‐mean‐square error of 67.01 m. The proposed method can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain aerosol vertical distribution on a global scale