Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

Citation:
Fischer, ML, Parazoo N, Brophy K, Cui XG, Jeong S, Liu JJ, Keeling R, Taylor TE, Gurney K, Oda T, Graven H.  2017.  Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations. Journal of Geophysical Research-Atmospheres. 122:3653-3671.

Date Published:

2017/03

Keywords:

(co2)-c-14, biosphere, carbon dioxide, co2 emissions, emissions, fluxes, fossil fuel, inversion, mission, performance, simulation

Abstract:

We report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO2, together with column-mean CO2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO2 exchange, in radiocarbon-based fossil fuel CO2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find that flask measurements of radiocarbon and total CO2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of similar to 5-10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO2 exchange of similar to 6%-12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.

Notes:

n/a

Website

DOI:

10.1002/2016jd025617