Increasing atmospheric CO2 measurements in North America, especially in urban areas, may help enable the development of an operational CO2 emission monitoring system. However, isolating the fossil fuel emission signal in the atmosphere requires factoring out CO2 fluctuations due to the biosphere, especially during the growing season. To help improve simulations of the biosphere, here we customize the Vegetation Photosynthesis and Respiration Model (VPRM) at high-resolution for an eastern North American domain, upwind of coastal cities from Washington D.C. to Boston, MA, optimizing parameters using domain-specific flux tower data from 2001 to the present. We run three versions of VPRM from November 2016 to October 2017 using i) annual (VPRMann) and ii) seasonal parameters (VPRMseas), and then iii) modifying the respiration equation to include the Enhanced Vegetation Index (EVI), a squared temperature term and interactions between temperature and water stress (VPRMnew). VPRM flux estimates are evaluated by comparison with other models (the Carnegie-Ames-Stanford Approach model, or CASA, and the Simple Biosphere Model v4), and with comparison to atmospheric CO2 mole fraction data at 21 surface towers. Results show that VPRMnew is relatively unbiased and outperforms all other models in explaining CO2 variability from April to October, while VPRMann overestimates growing season sinks by underestimating summertime respiration. Despite unknown remaining errors in VPRMnew, and uncertainties associated with other components of the atmospheric CO2 comparisons, VPRMnew appears to hold promise for more effectively separating anthropogenic and biospheric signals in atmospheric inversion systems in eastern North America.

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.

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.

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.