Spatial attribution of temporal variability in global land-atmosphere
CO2 exchange using a model-data integration framework
Abstract
The spatial contribution to the global land-atmosphere carbon dioxide
(CO\textsubscript{2}) exchange is crucial in
understanding and projecting the global carbon cycle, yet different
studies diverge on the dominant regions. Informing land models with
observational data is a promising way to reduce the parameter and
structural uncertainties and advance our understanding. Here, we develop
a parsimonious diagnostic process-based model of land carbon cycles,
constraining parameters with observation-based products. We compare
CO\textsubscript{2} flux estimates from our model with
observational constraints and Trends in Net Land-Atmosphere Carbon
Exchange (TRENDY) model ensemble to show that our model reasonably
reproduces the seasonality of net ecosystem exchange (NEE) and GPP and
interannual variability (IAV) of NEE. Finally, we use the developed
model, TRENDY models, and observational constraints to attribute
variability in global NEE and gross primary productivity (GPP) to
regional variability. The attribution analysis confirms the dominance of
Northern temperate and boreal regions in the seasonality of
CO\textsubscript{2} fluxes. Regarding NEE IAV, we
identify a significant contribution from tropical savanna regions as
previously perceived. Furthermore, we highlight that tropical humid
regions are also identified as at least equally relevant contributors as
semi-arid regions. At the same time, the largest uncertainty among
ensemble members of NEE constraint and TRENDY models in the tropical
humid regions underscore the necessity of better process understanding
and more observations in these regions. Overall, our study identifies
tropical humid regions as key regions for global land-atmosphere
CO\textsubscript{2} exchanges and the inter-model
spread of its modeling.