Seasonal Characteristics of Model Uncertainties From Biogenic Fluxes, Transport, and Large‐Scale Boundary Inflow in Atmospheric CO₂ Simulations Over North America

Abstract

Regional estimates of biogenic carbon fluxes over North America from both atmospheric inversions (“top‐down” approach) and terrestrial biosphere models (“bottom‐up”) remain highly uncertain. We merge these approaches with an ensemble‐based, regional modeling system able to diagnose and quantify the causes of uncertainties in top‐down atmospheric estimates of the terrestrial sink over North America. Our ensemble approach quantifies and partitions the uncertainty stemming from atmospheric transport, the biosphere, and large‐scale CO2 boundary inflow (boundary conditions). We use meteorological data, CO2 fluxes, and CO2 mole fraction measurements to assure the reliability of the ensemble system. Our results show that all uncertainty components have clear seasonal variations. The biogenic flux component dominates modeled boundary layer CO2 uncertainty, ranging from 2.5 ppm in summer and winter to 1.5 ppm in fall and spring. Spatially, it remains highly uncertain in the U.S. Corn Belt regions. Transport uncertainty reaches a maximum of 2.5 ppm in the summer months and stays at 1.2 ppm for the rest of the year and is highly correlated with the biogenic CO2 fluxes. Boundary conditions play the smallest role in atmospheric boundary layer CO2 uncertainty with a magnitude smaller than 1 ppm. However, boundary conditions are the most important uncertainty component in column‐averaged CO2 (XCO2). The spatiotemporal variations of the uncertainties in modeled XCO2 are similar to those in atmospheric boundary layer CO2.

Autonomous airborne mid-infrared spectrometer for high-precision measurements of ethane during the NASA ACT-America studies

“…To retrieve the measured concentration, Sect. 2.7 discusses further details of the fitting procedures employed in determining ambient ethane concentrations using background-subtracted spectra and measurements of cell pressure, temperature, and path length along with spectroscopic parameters from the infrared database provided by Harrison et al (2010) using the Beer-Lambert absorption law.…”

“…At power-up, the instrument runs a series of "checks" and if passed enters "ambient" measurement mode without the need to "calibrate" as the measurement is based on the first-principle Beer-Lambert absorption law. Figure 5 shows a simulation of the resulting absorption spectrum for 1 ppbv of ethane employing typical conditions of temperature (26.6 • C), pressure (73.2 torr), and a path length of 4760 cm using Voigt line profiles and the Harrison et al (2010) database for ethane. As discussed by Yacovitch et al (2014), the HITRAN database for ethane does not satisfactorily reproduce the ethane spectrum in the 3 µm region, and this was further verified by our comparisons of the direct absorption results with independently calibrated standards.…”

“…Employing the Marquardt-Levenberg nonlinear fitting algorithm (Marquardt, 1963;Levenberg, 1944) with these absorbance measurements along with spectral parameters from Harrison et al (2010) for ethane and from the 2016 HI-TRAN database for methane and water, Voigt line shapes, and measurements of pressure, temperature, and path length, we calculate the best-fit absorbance profile. The system soft- Figure 6.…”

“…Both the C-130 and B200 were deployed with similar payloads and coordinated flight paths to study the transport of greenhouse gases, primarily CO 2 and CH 4 , by midlatitude weather systems. Papers related to these activities are Pal et al (2020), Feng et al (2019), Zhou et al (2020), Barkley et al (2019a, b), and Bell et al (2020). Typical airborne ethane measurement precisions reported by Yacovitch et al (2014) and Smith et al (2015) were approximately 80 pptv, which is about a factor of 4 higher than when the aircraft was on the ground.…”

Development and evaluation of CO<sub>2</sub> transport in MPAS-A v6.3

“…Then the simulation results are evaluated using an extensive set of airborne observations over the eastern United States and near-surface observations from surface and tower stations across the globe. The simulation accuracy of MPAS is compared with three established CO 2 modeling systems based on the same observational data: WRF-Chem (Skamarock et al, 2008;Feng et al, 2019), Carbontracker (v2019, CT2019 hereafter) (Jacobson et al, 2020) , and ECWMF IFS (Agusti-Panareda et al, 2014, 2019.…”