A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems

Abstract

Independent verification and quantification of fossil fuel (FF) emissions constitutes a considerable scientific challenge. By coupling atmospheric observations of CO2 with models of atmospheric transport, inverse models offer the possibility of overcoming this challenge. However, disaggregating the biospheric and FF flux components of terrestrial fluxes from CO2 concentration measurements has proven to be difficult, due to observational and modeling limitations. In this study, we propose a statistical inverse modeling scheme for disaggregating winter time fluxes on the basis of their unique error covariances and covariates, where these covariances and covariates are representative of the underlying processes affecting FF and biospheric fluxes. The application of the method is demonstrated with one synthetic and two real data prototypical inversions by using in situ CO2 measurements over North America. Inversions are performed only for the month of January, as predominance of biospheric CO2 signal relative to FF CO2 signal and observational limitations preclude disaggregation of the fluxes in other months. The quality of disaggregation is assessed primarily through examination of a posteriori covariance between disaggregated FF and biospheric fluxes at regional scales. Findings indicate that the proposed method is able to robustly disaggregate fluxes regionally at monthly temporal resolution with a posteriori cross covariance lower than 0.15 µmol m−2 s−1 between FF and biospheric fluxes. Error covariance models and covariates based on temporally varying FF inventory data provide a more robust disaggregation over static proxies (e.g., nightlight intensity and population density). However, the synthetic data case study shows that disaggregation is possible even in absence of detailed temporally varying FF inventory data.

Large Uncertainties in Urban‐Scale Carbon Emissions

“…These deviations appear to reflect the spatial patterns in source activity that are well represented in ACES, particularly line source emissions from major roads, but that are not captured by the population-and nightlightsbased downscaling in ODIAC. While ODIAC and the other global inventories were not designed to be used for this type of urban analysis, they have been applied to a wide range of urban-and regional-scale modeling and policy analyses (e.g., Brioude et al, 2013;Hakkarainen et al, 2016;Marcotullio et al, 2012;Sarzynski, 2012;Schneising et al, 2013;Shiga et al, 2014;Tohjima et al, 2014;Turnbull et al, 2011;Vogel et al, 2012;Wunch et al, 2009;Yadav et al, 2016). Indeed, the high-resolution grids of these inventories (1-10 km) appear to Journal of Geophysical Research: Atmospheres 10.1002/2017JD027359 imply a fidelity at these local scales that is not necessarily evident.…”

Spatio‐temporally Resolved Methane Fluxes From the Los Angeles Megacity

“…In this work, California Greenhouse Gas Emissions Measurement (CALGEM) version 2.1 (e.g., Figure 1), an inventory of CH 4 emissions (Jeong et al, 2012), was prescribed as a covariate in X. A separate variance was computed for each tower in R, and a scaling factor was computed for Q whose off-diagonal entries were 0 and diagonal entries contained CALGEM emission estimates for each grid cell (e.g., Yadav et al, 2016).…”

“…The model resolution matrix is of considerable utility as it (1) has a definite lower and upper bound (range of 0-1), (2) encompasses information about the posterior uncertainty on the estimated fluxes, (3) conveys information regarding the spatiotemporal sensitivity of the network, and (4) leads to identification of the grid-scale fluxes that are constrained by the measurements. This matrix for GIM (e.g., Yadav et al, 2016) can be given as…”

Constraining sector-specific CO<sub>2</sub> and CH<sub>4</sub> emissions in the US

“…Third, a number of studies leverage a strategy known as geostatistical inverse modeling (GIM) to estimate GHG fluxes generally (e.g., Michalak et al, 2004;Gourdji et al, 2008Gourdji et al, , 2012 and anthropogenic emissions specifically Shiga et al, 2014;ASCENDS Ad Hoc Science Definition Team, 2015;Yadav et al, 2016). This approach attributes patterns in the emissions to individual anthropogenic source sectors when possible.…”

“…Many existing inventories do not have well-developed activity data for the oil and gas industry, and the unattributable emissions in Miller et al (2013) provide information about shortfalls in these activity datasets. Yadav et al (2016) modify the existing GIM framework to better isolate anthropogenic CO 2 emissions. The authors exploit differences in the spatiotemporal properties of biospheric versus fossil fuel fluxes to do this attribution.…”