A review of applications of model-data fusion to studies of terrestrial carbon fluxes at different scales

Abstract

Model-data fusion is defined as matching model prediction and observations by varying model parameters or states using statistical estimation. In this paper, we review the history of applications of various model-data fusion techniques in studies of terrestrial carbon fluxes in two approaches: top-down approaches that use measurements of global CO2 concentration and sometimes other atmospheric constituents to infer carbon fluxes from the land surface, and bottom-up approaches that estimate carbon fluxes using process-based models. We consider applications of model-data fusion in flux estimation, parameter estimation, model error analysis, experimental design and forecasting. Significant prog..