A Bayesian hierarchical model for CO₂ surface-flux estimation from multivariate satellite data

Quantifying the natural components of CO₂ surface flux is key to understanding Earth's carbon dynamics. Existing inverse methods struggle to isolate the natural components, which cannot be individually constrained using atmospheric CO₂ concentrations alone. However, the advent of solar-induced fluorescence (SIF) satellite data provides an opportunity to improve identifiability by constraining the distribution of the gross primary production (GPP) component. Here, we develop a spatio-temporal hierarchical model linking GPP to SIF and embed it within the WOMBAT v2.0 (WOllongong Methodology for Bayesian Assimilation of Trace-gases, version 2.0) statistical flux-inversion framework. We call the new framework WOMBAT v2.S, and we apply it to multivariate data from NASA's OCO-2 satellite to estimate natural flux components over the globe during a six-year period. In a simulation experiment that mimics OCO-2's retrieval characteristics, the inclusion of SIF substantially improves posterior accuracy and uncertainty quantification. Comparing real-data estimates from WOMBAT v2.S, v2.0, and an alternative method, we observe a reversal in the inferred trends of global CO₂ absorption.

flux inversion

spatio-temporal modeling

remote sensing

carbon cycle

gross primary production (GPP)

solar-induced fluorescence (SIF) 

Section on Statistics and the Environment