Global variability of carbon use efficiency in terrestrial ecosystems
Abstract. Vegetation carbon use efficiency (CUE) is a key measure of carbon (C) transfer from the atmosphere to terrestrial biomass, and indirectly reflects how much C is released through autotrophic respiration from the vegetation to the atmosphere. Diagnosing the variability of CUE with climate and other environmental factors is fundamental to understand its driving factors, and to further fill the current gaps in knowledge about the environmental controls on CUE. Thus, to study CUE variability and its driving factors, this study established a global database of site-year CUE based on observations from 188 field measurement sites for five ecosystem types – forest, grass, wetland, crop and tundra. The spatial pattern of CUE was predicted from global climate and soil variables using Random Forest, and compared with estimates from Dynamic Global Vegetation Models (DGVMs) from the TRENDY model ensemble. Globally, we found two prominent CUE gradients in ecosystem types and latitude, that is, CUE varied with ecosystem types, being the highest in wetlands and lowest in grassland, and CUE decreased with latitude with the lowest CUE in tropics, and the highest CUE in higher latitude regions. CUE varied greatly between data-derived CUE and TRENDY-CUE, but also among TRENDY models. Both data-derived and TRENDY-CUE challenged the constant value of 0.5 for CUE, independent of environmental controls. However, given the role of CUE in controlling the spatial and temporal variability of the terrestrial biosphere C cycle, these results emphasize the need to better understand the biotic and abiotic controls on CUE to reduce the uncertainties in prognostic land-process model simulations. Finally, this study proposed a new estimate of net primary production based on CUE and gross primary production, offering another benchmark for net primary production comparison for global carbon modelling.
This preprint has been withdrawn.
Xiaolu Tang et al.
Xiaolu Tang et al.
Xiaolu Tang et al.
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