Top-down studies have found consistent underestimations in the United States Environmental Protection Agency (EPA) methane emissions inventory from the oil and gas (O&G) sector. Many of these studies use observations that bias towards hours when worktime activity occurs. In this study we analyze over two years of methane measurements from a tower network in the Delaware basin to analyze hourly temporal emission patterns. Inversion results suggest a range in emissions from 134 Mg/hr at night to 196 Mg/hr during the day, present during both weekdays and weekends. If these results are applicable to other basins, daytime-influenced methodologies may overestimate daily emission rates by up to 27%. This bias does not reconcile the more than 200% difference between the EPA inventory and top-down estimates in the Delaware basin. This study demonstrates how continuous measurement networks can be combined with detailed activity data to improve bottom-up inventories.

Evaluating the efficacy of climate mitigation measures requires quantifying urban greenhouse gas (GHG) emissions. Both anthropogenic and biogenic GHG fluxes are important in urban systems, and disaggregation is necessary to understand urban GHG fluxes. One vegetative community common in these urban environments is turfgrass, and the Vegetation Photosynthesis and Respiration Model (VPRM), commonly used to create prior fluxes necessary for determining urban carbon dioxide (CO2) fluxes via inversion modeling, had assumed this Plant Functional Type (PFT) to be dormant in the winter. Two urban turfgrass flux towers in Indianapolis show a photosynthetic drawdown in the winter. We use data from these towers to create a new turfgrass PFT parameter set for VPRM. The new turfgrass PFT enables VPRM to simulate photosynthetic activity during winter, unlike the previously used parameter sets for grassland. We also find that less-managed lawns like cemeteries need different parameters than heavily managed lawns like golf courses, and seasonally changing parameters are needed to model these turf lawns. We use the revised VPRM to explore daily and seasonal variability in turfgrass fluxes and their impact, integrated across the city, on urban ecosystem CO2 fluxes. This study illustrates the importance of representing turfgrass as a unique PFT when quantifying urban GHG fluxes and the biases resulting from misrepresentation.

We evaluated the ability of a simple ecosystem carbon dioxide (CO2) flux model, the Vegetation Photosynthesis and Respiration Model (VPRM), to capture complex CO2 background conditions observed in Indianapolis, IN. Using simulated biogenic CO2 fluxes and mole fraction tower influence functions, we estimated biogenic CO2 mole fractions at three background towers in the Indianapolis Flux Experiment (INFLUX) network from April 2017 to March 2020. The model captures afternoon average CO2 enhancements, the difference between the background towers and a common reference tower, at a monthly time scale with no significant bias, with monthly mean residuals rarely differing significantly from zero. Although not central to our application, the model could not capture day-to-day variations of observed afternoon average CO2 enhancements. Random errors, when averaged over monthly to yearly time scales, were an order of magnitude smaller than typical urban enhancements. VPRM captured site-to-site differences in the average observed daily cycle of CO2 fluxes at agricultural eddy covariance flux sites well. For 13 of 14 site-months, the modeled peak afternoon NEE was within 30% of that observed despite the observed peaks ranging from about -7 to -70 µmol m-2s-1. VPRM can be effectively used in CO2 inversions to represent complex seasonal variations in background conditions observed in Indianapolis. Indianapolis, a modest-size city surrounded by strong ecosystem fluxes, represents a rigorous test for the VPRM system. Further, this study presents an evaluation system that can be applied to assess the performance of other ecosystem CO2 flux models in cities with similar monitoring networks.

Nitrous oxide (N2O), a potent greenhouse gas and ozone depleting substance, plays a crucial role in the atmosphere. Anthropogenic emissions from agriculture contribute to a rising trend in global N2O emissions and atmospheric concentrations. However, due to insufficient direct observations, regional N2O emissions derived in bottom-up and top-down studies are highly uncertain. The U.S. Midwest is one of the most intensive agriculture areas worldwide and hence may contribute significantly to the observed trend. Recent top-down studies suggest that bottom-up estimates underestimate agricultural emissions in that area by up to an order of magnitude. Here we quantify nitrous oxide emissions in the Midwest in October 2017 and June-July 2019 with a top-down approach. Unique continuous aircraft-based measurements of N2O conducted during the ACT-America campaign together with forward WRF-Chem model simulations are used to scale the EDGAR inventory thus quantifying emissions. On average we had to upscale October 2017 and June-July 2019 agricultural EDGAR 4.3.2/5.0 emissions by a factor of 6.3/3.5 and 11.4/9.9, resulting in 0.42 nmol m-2 s-1 and 1.06 nmol m-2 s-1 emissions in the Midwest, respectively. Finally, calculations of direct soil N2O emissions from the DayCent biogeochemical model are compared to our estimates.

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding events of varying intensity is poorly understood. To improve upon such understanding, the impact of the 2019 US flooding on regional CO2 vegetation fluxes is examined in the context of 2017-2018 years when such precipitation anomalies are not observed. CO2 is simulated with NASA’s Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion (LoFI), where fluxes of CO2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power and bias corrected based on in situ observations. Net ecosystem exchange CO2 tracer is separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15-25% reduction of net carbon uptake in May-September of 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, net carbon uptake is enhanced in May-September of 2019 by about 10-20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

In the last decade, much work has been done to better understand methane (CH4) emissions from the oil and gas (O&G) industry in the United States. Ethane (C2H6), a gas that is co-emitted with thermogenic sources of CH4 , is emitted in the US almost entirely by the O&G sector. In this study, we perform an inverse analysis on 300 hours of atmospheric boundary layer C2H6 measurements to estimate C2H6 emissions from the US O&G sector. Measurements were collected from 2017-2019 as part of the Atmospheric Carbon and Transport (ACT) America aircraft campaign and encompass much of the central and eastern United States. We find that for the fall, winter, and spring campaigns, C2H6 data consistently exceeds values that would be expected based on EPA O&G leak rate estimates. C2H6 observations from the summer 2019 dataset show significantly lower C2H6 enhancements in the southcentral region that cannot be reconciled with data from the other three seasons, either due to complex meteorological conditions or a temporal shift in the emissions. Converting the fall, winter, and spring season posterior C2H6 emissions estimate to an inventory of O&G CH4 emissions, we estimate that O&G CH4 emissions are larger than EPA inventory values by more than 50%. Uncertainties in the gas composition data limit the effectiveness of using C2H6 as a proxy for O&G CH4 emissions. These limits could be resolved retroactively by increasing the availability of industry-collected gas composition data.

We use 148 airborne vertical profiles of CO2 for frontal cases from the summer 2016 Atmospheric Carbon and Transport-America (ACT-America) campaign to evaluate the skill of ten global CO2 in situ inversion models from the version 7 Orbiting Carbon Observatory-2 (OCO-2) Model Intercomparison Project (MIP). Model errors (model posterior-observed CO2 dry air mole fractions) were categorized by region (Mid-Atlantic, Midwest, and South), frontal sector (warm or cold), and transport model (predominantly Tracer Model 5 (TM5) and Goddard Earth Observing System-Chemistry (GEOS-Chem)). All inversions assimilated the same CO2 observations. Overall, the median inversion profiles reproduce the general structures of the observations (enhanced / depleted low-level CO2 in warm / cold sectors), but 1) they underestimate the magnitude of the warm / cold sector mole fraction difference, and 2) the spread among individual inversions can be quite large (> 5 ppm). Uniquely in the Mid-Atlantic, inversion biases segregated according to atmospheric transport model, where TM5 inversions biases were-3 to-4 ppm in warm sectors, while those of GEOS-Chem were +2 to +3 ppm in cold sectors. The large spread among the mean posterior CO2 profiles is not explained by the different atmospheric transport models. These results show that the inversion systems themselves are the dominant cause of this spread, and that the aircraft campaign data are clearly able to identify these large biases. Future controlled experiments should identify which inversions best reproduce midlatitude CO2 mole fractions, and how inversion system components are linked to system performance.

The Atmospheric Carbon and Transport (ACT) – America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five, six-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America data set and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise sub-continental GHG flux estimates.

We evaluate terrestrial net ecosystem-atmosphere exchange (NEE) of CO2 from nine global inversion systems that inferred fluxes from four CO2 observational sources. We use 98 flights in the central and eastern U.S. from the ACT-America aircraft mission to conduct this sub-continental, seasonal-scale evaluation. We use Lagrangian particle dispersion modeling (FLEXPARTv10.4-ERA-Interim) to compare observed and simulated regional biogenic CO2 mole fractions. We find a positive bias (modeled CO2 > observed) in the summer and negative bias (modeled CO2 < observed) in dormant seasons across most flux products, suggesting that the seasonal strength of CO2 NEE is underestimated in these inverse models. Fluxes inferred from OCO-2 v9 satellite land nadir/glint observations yield an error level that is similar to fluxes inferred from in-situ data. Large bias errors are observed in the croplands and eastern forests. Future experiments are needed to determine if these seasonal biases are associated with biases in net annual flux estimates.

Enhanced CO2 mole fraction bands were often observed immediately ahead of cold front during the Atmospheric Carbon and Transport (ACT)-America mission and their formation mechanism is undetermined. Improved understanding and correct simulation of these CO2 bands are needed for unbiased inverse CO2 flux estimation. Such CO2 bands are hypothesized to be related to nighttime CO2 respiration and investigated in this study using WRF-VPRM, a weather-biosphere-online-coupled model, in which the biogenic fluxes are handled by the Vegetation Photosynthesis and Respiration Model (VPRM). While the default VPRM satisfactorily parameterizes gross ecosystem exchange, its treatment of terrestrial respiration as a linear function of temperature was inadequate as respiration is a nonlinear function of temperature and also depends on the amount of biomass and soil wetness. An improved ecosystem respiration parameterization including enhanced vegetation index, a water stress factor, and a quadratic temperature dependence is incorporated into WRF-VPRM and evaluated in a year-long simulation before applied to the investigation of the frontal CO2 band on 4 August 2016. The evaluation shows that the modified WRF-VPRM increases ecosystem respiration during the growing season, and improves model skill in reproducing nighttime near-surface CO2 peaks. A nested-domain WRF-VPRM simulation is able to capture the main characteristics of the 4 August CO2 band and informs its formation mechanism. Nighttime terrestrial respiration leads to accumulation of near-surface CO2 in the region. As the cold front carrying low-CO2 air moves southeastward, and strong photosynthesis depletes CO2 further southeast of the front, a CO2 band develops immediately ahead of the front.

The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America datasets provide airborne in-situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. We conducted 121 research flights during five campaigns in four seasons during 2016-2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 hours of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in-situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of datasets collected, and the workflow for datasets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of datasets for future investigations.

To check the accuracy of column-average dry air CO2 mole fractions (“XCO2”) retrieved from Orbiting Carbon Overvatory (OCO-2) data, a similar quantity has been measured from the Multi-functional Fiber Laser Lidar (MFLL) aboard aircraft flying underneath OCO-2 as part of the Atmospheric Carbon and Transport (ACT)-America flight campaigns. Here we do a lagged correlation analysis of these MFLL-OCO-2 column CO2 differences and find that their correlation spectrum falls off rapidly at along-track separation distances of under 10 km, with a correlation length scale of about 10 km, and less rapidly at longer separation distances, with a correlation length scale of about 20 km. The OCO-2 satellite takes many CO2 measurements with small (∼3 km^2) fields of view (FOVs) in a thin (~10 km wide) swath running parallel to its orbit: up to 24 separate FOVs may be obtained per second (across a ∼6.75 km distance on the ground), though clouds, aerosols, and other factors cause considerable data dropout. Errors in the CO2 retrieval method have long been thought to be correlated at these fine scales, and methods to account for these when assimilating these data into 10 top-down atmospheric CO2 flux inversions have been developed. A common approach has been to average the data at coarser scales (e.g., in 10-second-long bins) along-track, then assign an uncertainty to the averaged value that accounts for the error correlations. Here we outline the methods used up to now for computing these 10-second averages and their uncertainties, including the constant-correlation-with-distance error model currently being used to summarize the OCO-2 version 9 XCO2 retrievals as part of the OCO-2 flux inversion model intercomparison project. We then derive a new one-dimensional error model using correlations that decay exponentially with separation distance, apply this model to the OCO-2 data using the correlation length scales derived from the MFLL-OCO-2 differences, and compare the results (for both the average and its uncertainty) to those given by the current constant-correlation error model. To implement this new model, the data are averaged first across 2-second spans, to collapse the cross-track distribution of the real data onto the 1-D path assumed by the new model. A small percentage of the data that cause non-physical negative averaging weights in the model are thrown out. The correlation lengths over the ocean, which the land-based MFLL data do not clarify, are assumed to be twice those over the land. The new correlation model gives 10-second XCO2 averages that are only a few tenths of a ppm different from the constant-correlation model. […] Finally, we show how our 1-D exponential error correlation model may be used to account for correlations in those inversion methods that choose to assimilate each XCO2 retrieval individually, and to account for correlations between separate 10-second averages when these are assimilated instead.