Improved urban greenhouse gas (GHG) flux estimates are crucial for informing policy and mitigation efforts. Atmospheric inversion modelling (AIM) is a widely used technique combining atmospheric measurements of trace gas, meteorological modelling, and a prior emission map to infer fluxes. Traditionally, AIM relies on mid-afternoon observations due to the well-represented atmospheric boundary layer in meteorological models. However, confining flux assessement to daytime observations is problematic for the urban scale, where air masses typically move over a city in a few hours and AIM therefore cannot provide improved constraints on emissions over the full diurnal cycle. We hypothesized that there are atmospheric conditions beyond the mid-afternoon under which meteorological models also perform well. We tested this hypothesis using tower-based measurements of CO2 and CH4, wind speed observations, weather model outputs from INFLUX (Indianapolis Flux Experiment), and a prior emissions map. By categorizing trace gas vertical gradients according to wind speed classes and identifying when the meteorological model satisfactorily simulates boundary layer depth (BLD), we found that non-afternoon observations can be assimilated when wind speed is >5 m/s. This condition resulted in small modeled BLD biases (<40%) when compared to calmer conditions (>100%). For Indianapolis, 37% of the GHG measurements meet this wind speed criterion, almost tripling the observations retained for AIM. Similar results are expected for windy cities like Auckland, Melbourne, and Boston, potentially allowing AIM to assimilate up to 60% the total (24-h) observations. Incorporating these observations in AIMs should yield a more diurnally comprehensive evaluation of urban GHG emissions.

Extreme climate events are becoming more frequent, with poorly understood implications for carbon sequestration by terrestrial ecosystems. A better understanding will critically depend on accurate and precise quantification of ecosystems responses to these events. Taking the 2019 US Midwest floods as a case study, we investigate current capabilities for tracking regional flux anomalies with “top-down” inversion analyses that assimilate atmospheric CO2 observations. For this analysis, we develop a regionally nested version of the NASA Carbon Monitoring System-Flux (CMS-Flux) that allows high resolution atmospheric transport (0.5° × 0.625°) over a North America domain. Relative to a 2018 baseline, we find US Midwest growing season net carbon uptake is reduced by 11-57 TgC (3-16%) for 2019 (inversion mean estimates across experiments). These estimates are found to be consistent with independent “bottom-up” estimates of carbon uptake based on vegetation remote sensing. We then investigate current limitations in tracking regional carbon emissions and removals by ecosystems using “top-down” methods. In a set of observing system simulation experiments, we show that the ability to recover regional carbon flux anomalies is still limited by observational coverage gaps for both in situ and satellite observations. Future space-based missions that allow for daily observational coverage across North America would largely mitigate these observational gaps, allowing for improved top-down estimates of ecosystem responses to extreme climate events.

Synoptic weather systems are a major driver of spatial gradients in atmospheric CO2 mole fractions. During frontal passages, air masses from different regions meet at the frontal boundary creating significant gradients in CO2 mole fractions. We quantitatively describe the atmospheric transport of CO2 mole fractions during a mid-latitude cold front passage and explore the impact of various sources of CO2. We focus here on a cold front passage over Lincoln, Nebraska on August 4th, 2016 observed by aircraft during the Atmospheric Carbon and Transport (ACT)-America campaign. A band of air with elevated CO2was located along the frontal boundary. Observed and simulated differences in CO2 across the front were as high as 25 ppm. Numerical simulations using WRF-Chem at cloud resolving resolutions (3km), coupled with CO2 surface fluxes and boundary conditions from CarbonTracker (CT-NRTv2017x), were performed to explore atmospheric transport at the front. Model results demonstrate that the frontal CO2 difference in the upper troposphere can be explained largely by inflow from outside of North America. This difference is modified in the atmospheric boundary layer and lower troposphere by continental surface fluxes, dominated in this case by biogenic and fossil fuel fluxes. Horizontal and vertical advection are found to be responsible for the transport of CO2 mole fractions along the frontal boundary. We show that cold front passages lead to large CO2 transport events including a significant contribution from vertical advection, and that mid-continent frontal boundaries are formed from a complex mixture of CO2 sources.

The ability of current global models to simulate the transport of CO2 by mid-latitude, synoptic-scale weather systems (i.e. CO2 weather) is important for inverse estimates of regional and global carbon budgets but remains unclear without comparisons to targeted measurements. Here, we evaluate ten models that participated in the Orbiting Carbon Observatory-2 model intercomparison project (OCO-2 MIP version 9) with intensive aircraft measurements collected from the Atmospheric Carbon Transport (ACT)-America mission. We quantify model-data differences in the spatial variability of CO2 mole fractions, mean winds, and boundary layer depths in 27 mid-latitude cyclones spanning four seasons over the central and eastern United States. We find that the OCO-2 MIP models are able to simulate observed CO2 frontal differences with varying degrees of success in summer and spring, and most underestimate frontal differences in winter and autumn. The models may underestimate the observed boundary layer-to-free troposphere CO2 differences in spring and autumn due to model errors in boundary layer height. Attribution of the causes of model biases in other seasons remains elusive. Transport errors, prior fluxes, and/or inversion algorithms appear to be the primary cause of these biases since model performance is not highly sensitive to the CO2 data used in the inversion. The metrics presented here provide new benchmarks regarding the ability of atmospheric inversion systems to reproduce the CO2 structure of mid-latitude weather systems. Controlled experiments are needed to link these metrics more directly to the accuracy of regional or global flux estimates.

We present observations of local enhancements in carbon dioxide (CO2) from local emissions sources over three eastern US regions during four deployments of the Atmospheric Carbon Transport-America (ACT-America) campaign between summer 2016 and spring 2018. Local CO2 emissions were characterized by carbon monoxide (CO) to CO2 enhancement ratios (i.e. ΔCO/ΔCO2) in airmass mixing observed during aircraft transects within the atmospheric boundary layer. By analyzing regional-scale variability of CO2 enhancements as a function of ΔCO/ΔCO2 enhancement ratios, observed relative contributions to CO2 emissions were contrasted between different combustion regimes across regions and seasons. Ninety percent of observed summer combustion in all regions was attributed to high efficiency fossil fuel (FF) combustion (ΔCO/ΔCO2 < 0.5%). In other seasons, regional contributions increased from less efficient forms of FF combustion (ΔCO/ΔCO2 0.5-2%) to as much as 60% of observed combustion. CO2 emission contributions attributed to biomass burning (BB) (ΔCO/ΔCO2 > 4%) were negligible during summer and fall in all regions, but climbed to 10-12% of observed combustion in the South during winter and spring. Vulcan v3 CO2 2015 emission analysis showed increases in residential and commercial sectors seasonally matching increases in less efficient FF combustion, but could not explain regional trends. WRF-Chem modeling, driven by CarbonTracker CO2 fire emissions, matched observed winter and spring BB contributions, but conflictingly predicted similar levels of BB during fall. Satellite fire data from MODIS and VIIRS suggested higher spatial resolution fire data might improve modeled BB emissions.

The ACT-America Earth Venture mission conducted five airborne campaigns across four seasons from 2016-2019, to study the transport and fluxes of Greenhouse gases across the eastern United States (US). Unprecedented spatial sampling of atmospheric tracers (CO2, CO, and COS) related to biospheric processes offers opportunities to improve our qualitative and quantitative understanding of seasonal and spatial patterns of biospheric carbon uptake. Here, we examine co-variation of boundary layer enhancements of CO2, CO, and COS across three diverse regions: the crop-dominated Midwest, evergreen-dominated South, and deciduous broadleaf-dominated Northeast. To understand the biogeochemical processes controlling these tracers, we compare the observed co-variation to simulated co-variation resulting from model- and satellite- constrained surface carbon fluxes. We found indication of a common terrestrial biogenic sink of CO2 and COS and secondary production of CO from biogenic sources in summer throughout the eastern US. Stomatal conductance likely drives fluxes through diffusion of CO2 and COS into leaves and emission of biogenic volatile organic compounds into the atmosphere. ACT-America airborne campaigns filled a critical sampling gap in the southern US, providing information about seasonal carbon uptake in southern temperate forests, and demanding a deeper investigation of underlying biological processes and climate sensitivities. Satellite- constrained carbon fluxes capture much of the observed seasonal and spatial variability, but underestimate the magnitude of net CO2 and COS depletion in the Southeast, indicating a stronger than expected net sink in late summer.

The U.S. Midwest, with its intensive agriculture, is a prominent source of nitrous oxide (N2O) but top-down and bottom-up N2O emission estimates differ significantly. We quantify Midwest N2O emissions by combining observations from the Atmospheric Carbon and Transport-America campaign with model simulations to scale the Emissions Database for Global Atmospheric Research (EDGAR). In October 2017 we increased agricultural EDGAR version 4.3.2/5.0 emissions by a factor of 6.3±4.6/3.5±2.7, resulting in Midwest N2O emissions of 0.42±0.28 nmol m-2 s-1. In June/July 2019, a period when extreme flooding was occurring in the Midwest, EDGAR was increased by a factor of 11.4±6.6/9.9±5.7, resulting in N2O emissions of 1.06±0.57 nmol m-2 s-1. Agricultural emissions estimated with the process-based model DayCent (Daily version of the CENTURY ecosystem model) were larger than in EDGAR but still substantially smaller than our estimates. Due to the complexity of N2O emissions, further studies are necessary to fully characterize Midwest emissions.

We present the first quantitative comparison of source-partitioned CO2 flux measurements with a high-resolution urban fossil CO2 emissions inventory. We use tower-based measurements of CO and 14C to partition net CO2 flux measurements into fossil and biogenic components in a suburban environment. A flux footprint model is used to quantify spatial patterns in fluxes. The partitioned fossil CO2 emissions are compared to a 200-m resolution emissions inventory (Hestia). The results indicate that Hestia and the partitioned flux data agree remarkably well on a seasonal average scale. The Hestia inventory is biased by 3.2% (cold season) and 9.1% (warm season). Their temporal-spatial patterns match closely. In addition, biogenic CO2 uptake is 25% of local fossil emissions during afternoon in the cold season. This work demonstrates the effectiveness of using eddy-covariance flux measurements both for evaluating urban emissions inventories and for quantifying urban ecosystem fluxes.

Terrestrial biosphere models (TBMs) play a key role in detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. This study uses atmospheric CO2 mole fraction ([CO2]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the CASA TBM. WRF-Chem is used to simulate [CO2] using biogenic CO2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO2] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO2] data show that the CASA ensemble net ecosystem exchange (NEE) of CO2 is biased high (NEE too positive) and identify the maximum light use efficiency Emax a key parameter that drives the spread of the CASA ensemble. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements indicates that modeled [CO2] biases are mainly due to missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilates observed [CO2]. Flux tower analyses, however, reveal unrealistic overestimation of nighttime respiration in CT2017.