Constraining a data-driven CO2 flux model by ecosystem and atmospheric observations using atmospheric transport
Résumé
Global estimates of the terrestrial land-atmosphere flux of CO2 (NEE) from data-driven models differ widely depending on their underlying data and methodology. Bottom-up models trained on eddy-covariance data are most informative at the ecosystem-level. Top-down models, such as atmospheric inversions, produce regional and global results consistent with the 5 observed atmospheric growth rate, accurately capturing the interannual variability (IAV) of NEE. Both approaches have limitations estimating NEE across scales: Bottom-up models can miss large-scale dynamics of NEE when aggregated globally. Top-down approaches have difficulty relating the large-scale atmospheric signal to biophysical processes at smaller scales. To address these limitations, we create a model that uses a hybrid combination of direct observations and atmospheric dynamics to integrate ecosystem-level eddy-covariance data and atmospheric CO2 mole fraction data into a single coherent ecosystem-level 10 flux model.
Auteurs, date et publication :
Auteurs Samuel Upton , Markus Reichstein , Wouter Peters , Santiago Botía , Jacob A. Nelson , Sophia Walther , Martin Jung , Fabian Gans , László Haszpra , Ana Bastos
Date : 2025