Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework
Michael Bertolacci, Andrew Zammit-Mangion, Andrew Schuh, Beata Bukosa,, Jenny Fisher, Yi Cao, Aleya Kaushik, Noel Cressie

TL;DR
This paper introduces WOMBAT v2.0, a hierarchical flux-inversion framework that uses a novel decomposition method to infer and analyze changes in the global carbon cycle from satellite and ground data, revealing significant trends and seasonal shifts.
Contribution
WOMBAT v2.0 is a new flux-inversion method that incorporates spatially varying time-series decomposition and physical constraints, improving the inference of carbon flux changes over large scales.
Findings
Tropical ecosystems shifted from net sources to net sinks of CO₂.
Global seasonal cycle amplitude increased by 8%.
Northern regions experienced earlier seasonal flux peaks.
Abstract
The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO) and other important greenhouse gases are changing in response to human influences. These changes need to be quantified to understand climate change and its impacts, but this is difficult to do because natural fluxes occur over large spatial and temporal scales. To infer trends in fluxes and identify phase shifts and amplitude changes in flux seasonal cycles, we construct a flux-inversion system that uses a novel spatially varying time-series decomposition of the fluxes, while also accommodating physical constraints on the fluxes. We incorporate these features into the Wollongong Methodology for Bayesian Assimilation of Trace-gases (WOMBAT, Zammit-Mangion et al., Geosci. Model Dev., 15, 2022), a hierarchical flux-inversion framework that yields posterior distributions for all unknowns in the underlying…
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Taxonomy
TopicsAtmospheric and Environmental Gas Dynamics · Geochemistry and Geologic Mapping · Hydrocarbon exploration and reservoir analysis
