Diagnosing spatial and temporal variations in the response of carbon use efficiency to vegetation states and climate across terrestrial ecosystems

        Carbon use efficiency (CUE) of vegetation is a property emerging from physiological processes, a key parameter to determine vegetation growth, which ultimately reflects the relative potential of terrestrial ecosystems to store atmospheric carbon in biomass. Large uncertainties were found in global CUE estimated by remote sensing data models or process-based models, especially across plant functional types (PFTs) and regarding seasonal variations. This study explores the specific effects of climate and vegetation state on CUE based on a model-data integration approach by analyzing outputs from a terrestrial ecosystem model driven with local meteorological variables and constrained by in situ observations of vegetation biomass, as well as carbon and water fluxes from eddy covariance measurements. We leverage on a modular model-data-integration framework – SINDBAD – that allows for an integrated model selection, parameterization and evaluation approach based on in situ observations. In addition, various other simulations based on global observations and global modeling approaches, including MODIS, GLASS, Trendy, MsTMIP, and CMIP6, are further explored with the aim of examining spatial and temporal patterns of CUE to better understand the modelled biological and climate controls of CUE.

        The range of global annual CUE values for the 50 models between 2001 and 2010 is 0.3083 to 0.5920. Our study shows that adding constraints on modelled vegetation biomass, in general, brings slight deterioration to the simulation of carbon fluxes but significantly changes the patterns of CUE. In general, biomass constraints decrease the emerging CUE estimates, even in non-forested sites in the Northern Hemisphere. The vegetation state constraints increase 14.42% of sites distributed at 0.2-0.4 median CUE yearly values and decrease 14.92% of sites distributed at 0.4-0.6. Constraints on vegetation carbon stocks result in changes in modelled autotrophic respiration, which change more significantly across sites than gross primary productivity. The information provided by the vegetation state variables results generally in lower wood and slow litter turnover rate, and an increased sensitivity of CUE to moisture and adaptation to temperature. Ultimately, here we provide model-based results for investigating the mechanisms behind the spatial and temporal variability of CUE, potentially contributing to a better quantified CUE variation globally.