Assimilation of multiple different datasets results in large differences in regional to global-scale NEE and GPP budgets simulated by a terrestrial biosphere model

Abstract. In spite of the importance of land ecosystems in offsetting carbon dioxide emissions released by anthropogenic activities into the atmosphere, the spatio-temporal dynamics of the carbon fluxes remain largely uncertain at regional to global scales. Over the past decade, Data Assimilation (DA) techniques have grown in importance for improving these fluxes simulated by Terrestrial Biosphere Models (TBMs), by optimizing model parameter values while also pinpointing possible parameterization deficiencies. Although the joint assimilation of multiple data streams is expected to constrain a wider range of model processes, their actual benefits in terms of reduction in model uncertainty are still under-researched, also given the technical challenges. In this study, we investigated with a consistent DA framework and the ORCHIDEE-LMDz TBM-atmosphere model how the assimilation of different combinations of data streams may result in different regional to global carbon budgets. To do so, we performed comprehensive DA experiments where three datasets (in situ measurements of net carbon exchange and latent heat fluxes, space-borne estimates of the Normalized Difference Vegetation Index, and atmospheric CO₂ concentration data at stations) are assimilated alone or simultaneously. We thus evaluated their complementarity and usefulness to constrain net and gross C fluxes. We found that a major challenge in improving the spatial distribution of the land sinks/sources with atmospheric CO₂ data relates to the correction of the initial carbon stocks.