1Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
2College of Earth Science, Chengdu University of Technology, Chengdu, Sichuan, China
3State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, China
4Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciênciase Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, Portugal
5Key laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing, P.R. China
6State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
7School of Life Science, University of Technology Sydney, NSW, Australia
8CNR – Institute for Mediterranean Agricultural and Forest Systems, Via Patacca 85, Ercolano (Napoli), Italy
9UMR ECOSYS, INRA-AgroParisTech, Université Paris Saclay, Thiverval-Grignon, France
10Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
11Department of Forestry and Natural Resources, Purdue University, 715 W. State St, West Lafayette, IN, USA
12Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Jinming Avenue, Kaifeng, China
13State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, Sichuan, China
14German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
15Michael Stifel Center Jena (MSCJ) for Data-Driven & Simulation Science, 07743 Jena, Germany
1Department Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany
2College of Earth Science, Chengdu University of Technology, Chengdu, Sichuan, China
3State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu, China
4Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciênciase Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, Portugal
5Key laboratory of Bamboo and Rattan, International Centre for Bamboo and Rattan, Beijing, P.R. China
6State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing, China
7School of Life Science, University of Technology Sydney, NSW, Australia
8CNR – Institute for Mediterranean Agricultural and Forest Systems, Via Patacca 85, Ercolano (Napoli), Italy
9UMR ECOSYS, INRA-AgroParisTech, Université Paris Saclay, Thiverval-Grignon, France
10Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
11Department of Forestry and Natural Resources, Purdue University, 715 W. State St, West Lafayette, IN, USA
12Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University, Jinming Avenue, Kaifeng, China
13State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu, Sichuan, China
14German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
15Michael Stifel Center Jena (MSCJ) for Data-Driven & Simulation Science, 07743 Jena, Germany
Received: 29 Jan 2019 – Accepted for review: 13 Feb 2019 – Discussion started: 14 Feb 2019
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.
Vegetation CUE is a key measure of carbon transfer from the atmosphere to terrestrial biomass. This study modelled global CUE with published observations using random forest. CUE varied with ecosystem types and spatially decreased with latitude, challenging the previous conclusion that CUE was independent of environmental controls. Our results emphasize a better understanding of environmental controls on CUE to reduce uncertainties in prognostic land-process model simulations.
Vegetation CUE is a key measure of carbon transfer from the atmosphere to terrestrial biomass....
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