Atmospheric nitrous oxide (N2O) is, after carbon dioxide and methane, the third most important long-lived anthropogenic greenhouse gas in terms of radiative forcing. Since preindustrial times a rising trend in the global N2O concentrations is observed. Anthropogenic emissions of N2O, mainly from agricultural activity, contribute considerably to this trend. Sparse observational constraints have made it difficult to quantify these emissions. The few studies on top-down approaches in the U.S. that exist are mainly based on Lagrangian models and ground-based measurements. They all propose a significant underestimation of anthropogenic N2O emission sources in established inventories, such as the Emissions Database for Global Atmospheric Research (EDGAR). In this study we quantify anthropogenic N2O emissions in the Midwest of the U.S., an area of high agricultural activity. In the course of the Atmospheric Carbon and Transport – America (ACT-America) campaign spanning from summer 2016 to summer 2019, an extensive dataset over four seasons has been collected including in-situ N2O aircraft based measurements in the lower and middle troposphere onboard NASA’s C-130 and B-200 aircraft. During fall 2017 and summer 2019 we conducted measurements onboard the NASA-C130 with a Quantum-Cascade-Laser-Spectrometer (QCLS) and on both aircraft over the whole campaign flask measurements (NOAA) were collected. More than 300 joint flight hours were conducted and more than 500 flask samples were collected over the U.S. Midwest. The QCLS system collected continuous N2O data for approximately 60 flight hours in this region. The Eulerian Weather Research and Forecasting model with chemistry enabled (WRF-Chem) is being used to quantify regional agricultural N2O emissions using the spatial characteristics of these atmospheric N2O mole fraction observations. The numerical simulations enable potential surface emission distributions to be compared to our airborne measurements, and source estimates can be adjusted to minimize the differences, thus quantifying N2O sources. These results are then compared to emission rates in the EDGAR inventory.

Quantification of regional terrestrial carbon dioxide (CO2) fluxes is critical to our understanding of the carbon cycle. We evaluate inverse estimates of net ecosystem exchange (NEE) of CO2 fluxes in temperate North America, and their sensitivity to the observational data used to drive the inversions. Specifically, we consider the state-of-the-science CarbonTracker global inversion system, which assimilates (i) in situ measurements (’IS’), 29 (ii) the Orbiting Carbon Observatory-2 (OCO-2) v9 column CO 2 (XCO2) retrievals over land (’LNLG’), (iii) OCO-2 v9 XCO 2 retrievals over ocean (’OG’), and (iv) a combination of all these observational constraints (’LNLGOGIS’). We use independent CO2 observations from the Atmospheric Carbon and Transport (ACT)-America aircraft mission to evaluate the inversions. We diagnose errors in the flux estimates using the differences between modeled and observed biogenic CO2 mole fractions, influence functions from a Lagrangian transport model, and root-mean-square error (RMSE) and bias metrics. The IS fluxes have the smallest RMSE among the four products, followed by LNLG. Both IS and LNLG outperform the OG and LNLGOGIS inversions with regard to RMSE. Regional errors do not differ markedly across the four sets of posterior fluxes. The CarbonTracker inversions appear to overestimate the seasonal cycle of NEE in the Midwest and Western Canada, and overestimate dormant season NEE across the Central and Eastern US. The CarbonTracker inversions may overestimate annual NEE in the Central and Eastern US. The success of the LNLG inversion with respect to independent observations bodes well for satellite-based inversions in regions with more limited in situ observing networks.

Atmospheric CO2 inversion typically relies on the specification of prior flux and atmospheric model transport errors, which have large uncertainties. Here, we use ACT-America 30 airborne observations to compare total CO 2 model-observation mismatch in the eastern U.S. and during four climatological seasons for the mesoscale WRF(-Chem) and global scale CarbonTracker/TM5 (CT) models. Models used identical surface carbon fluxes, and CT was used as CO 2 boundary condition for WRF. Both models show reasonable agreement with observations, and CO 2 residuals follow near symmetric peaked (i.e. non-Gaussian) distribution with near zero bias of both models (CT: −0.34 +/- 3.12 ppm; WRF: 0.82 +/- 4.37 ppm). We also encountered large magnitude residuals at the tails of the distribution that contribute considerably to overall bias. Atmospheric boundary-layer biases (1-10 ppm) were much larger than free tropospheric biases (0.5-1 ppm) and were of same magnitude as model-model differences, whereas free tropospheric biases were mostly governed by CO2 background conditions. Results revealed systematic differences in atmospheric transport, most pronounced in the warm and cold sectors of synoptic systems, highlighting the importance of transport for CO2 residuals. While CT could reproduce the principal CO2 dynamics associated with synoptic systems, WRF showed a clearer distinction for CO2 differences across fronts. Variograms were used to quantify spatial coherence of residuals and showed characteristic residual length scales of approximately 100 km to 300 km. Our findings suggest that inclusion of synoptic weather-dependent and non-Gaussian error structure may benefit inversion systems.

An agricultural watershed, Cole Farm, was established as the newest of the three subcatchments in the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) in 2017. The catchment contains mostly pasture and crops, with a small portion of deciduous forest. The observations in Cole Farm afford an opportunity to test the spatially distributed land surface hydrologic model, Flux-PIHM, in farmland for the first time. In this study, we calibrated the model to only discharge and groundwater level observations at Cole Farm, but it’s able to capture the variations and magnitudes of soil moisture, latent heat (LE) and sensible heat (H) fluxes. Modeled soil moisture on the ridge top matched the observations well, but modeled soil moisture in the mid-slope differed from observations likely due to the existence of fragipan in the soil column. Flux-PIHM reproduced the seasonality and diurnal variations of watershed-average evapotranspiration (ET), sensible heat flux (H), though modeled ET in summer is about 25% greater than tower ET. To study the impact of land cover on hydrology, we imposed two different LAI forcings to the model: spatially distributed versus uniform LAI. Spatially distributed LAI produced higher ET and lower soil moisture in the forested part of the watershed due to higher LAI of deciduous forest in comparison to crops and pasture. But the impact of different LAI forcings on discharge was small. We further compared the water budget simulated by Flux-PIHM in the agricultural watershed (Cole Farm) to a forested watershed (Shale Hills). Flux-PIHM simulated less discharge and higher transpiration and bare soil evaporation in the Cole Farm watershed relative to Shale Hills watershed. Our work shows that with a few key observations, Flux-PIHM can be calibrated to simulate agricultural watershed hydrology, but spatially distributed LAI and soils data are needed to capture the spatial variations in soil moisture and ET.