Incorporating physical model uncertainty in remote sensing estimates of atmospheric greenhouse gases

Music City Center 

A growing constellation of Earth-observing satellites are providing new opportunities to monitor the planet's changing carbon cycle through global estimates of atmospheric greenhouse gases (GHGs), including carbon dioxide (CO₂). The satellite record provides comprehensive spatial coverage, enabling continental-scale partitioning of the natural carbon exchanges and perturbations due to anthropogenic sources. These carbon cycle science investigations are sensitive to uncertainty in the satellite CO₂ estimates, or retrievals. Satellite retrievals result from a Bayesian formulation that combines observed satellite spectra and prior information on the atmospheric composition with a physical forward model. This retrieval approach incorporates uncertainty in the prior state and measurement noise in the satellite spectra, but other physical parameters are estimated offline without accounting for their additional uncertainty. This presentation will highlight an alternative hierarchical model (HM) formulation that incorporates uncertainty in these parameters and illustrates the impact on the resulting uncertainty in atmospheric CO₂ using data from NASA's Orbiting Carbon Observatory-2 and -3 (OCO-2/3) missions. The comprehensive handling of parameter, geophysical state, and physical model uncertainty in the HM will be demonstrated for a range of geophysical conditions.

remote sensing

carbon dioxide

hierarchical model

uncertainty quantification