Meeting Documents

Analytical Estimation of Carbon Dioxide Fluxes and Information Content from OCO-2 Satellite Data

Abstract

Satellite measurements of carbon dioxide (CO₂) provide an important tool to improve our understanding of the global carbon cycle in a changing climate. Inverse methods that use these observations to estimate the surface-atmosphere exchange of CO₂ are increasingly used in policy applications such as the Global Stocktake under the Paris Agreement. Quantifying the uncertainty of these fluxes is critical to the effective use of these estimates, including determining where and at what spatiotemporal resolution satellites can constrain fluxes. We estimate monthly CO₂ fluxes and uncertainties for 2015 to 2022 on a multiscale grid using atmospheric CO₂ column observations from the Orbiting Carbon Observatory-2 (OCO-2) instrument. The multiscale grid preserves the native 4^o x 5^o resolution in areas with high observational density and goes to coarser resolution elsewhere.

The optimized fluxes, associated uncertainties, and information content are generated by analytical minimization of a Bayesian cost function regularized by an initial (prior) flux estimate. We demonstrate a new method for characterizing the full prior error covariance matrix for the biosphere and oceans, for which initial estimates are provided by the CARbon DAta-MOdel fraMework (CARDAMOM) and the ECCO-Darwin ocean biogeochemistry state estimate. We also include fossil fuel uncertainty in our inversion framework. We show the influence of these error assumptions on our inferred fluxes with reference to the preliminary OCO MIP results. The analysis is developed to be reproducible by expanding the Integrated Methane Inversion into the Integrated Carbon Inversion using the new GEOS-Chem carbon simulation. It therefore supports future development, including increased spatiotemporal resolution and the incorporation of additional datasets such as other trace gas observations.