Inferring changes to the global carbon cycle with WOMBAT v2.0, a hierarchical flux-inversion framework
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The natural cycles of the surface-to-atmosphere fluxes of carbon dioxide (CO_2) 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 model. We apply the new method, which we call WOMBAT v2.0, to a mix of satellite observations of CO_2 mole fraction from the Orbiting Carbon Observatory-2 (OCO-2) satellite and direct measurements of CO_2 mole fraction from a variety of sources. We estimate the changes to CO_2 fluxes that occurred from January 2015 to December 2020, and compare our posterior estimates to those from an alternative method based on a bottom-up understanding of the physical processes involved. We find substantial trends in the fluxes, including that tropical ecosystems trended from being a net source to a net sink of CO_2 over the study period. We also find that the amplitude of the global seasonal cycle of ecosystem CO_2 fluxes increased over the study period by 0.11 PgC/month (an increase of 8 temperate and northern boreal regions shifted earlier in the year by 0.4-0.7 and 0.4-0.9 days, respectively (2.5th to 97.5th posterior percentiles).
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