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Global patterns of vegetation carbon use efficiency and their climate drivers deduced from MODIS satellite data and process-based models

He, Yue, Piao, Shilong, Li, Xiangyi, Chen, Anping, Qin, Dahe
Agricultural and forest meteorology 2018 v.256-257 pp. 150-158
carbon, carbon dioxide, carbon sequestration, climatic factors, data collection, ecosystems, gross primary productivity, latitude, model validation, models, moderate resolution imaging spectroradiometer, net primary productivity, remote sensing, satellites, temperature, vegetation
Carbon use efficiency (CUE), defined as the ratio of net primary production (NPP) to gross primary production (GPP), represents the capacity of plants in converting assimilated atmospheric carbon dioxide to ecosystem carbon storage. Process-based models are important tools for simulating NPP and GPP; yet the model performance in simulating vegetation CUE has not been fully explored. The goal of this paper is thus to investigate the spatial variations in CUE from different process-based carbon cycle models in comparison with that from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and to analyze their linkage with climate factors. The global average CUE derived from the five process-based models is 0.45 ± 0.05 (range from 0.38 to 0.52), slightly lower than the value of 0.48 obtained from MODIS data. A strong latitudinal gradient of CUE, with greater CUE at high latitudes, is well agreed by these different datasets. However, there also exist considerable discrepancies in CUE estimations among those products, especially in temperate Northern Hemisphere. Furthermore, for both the satellite-based dataset and results from process-based models, vegetation CUE declines non-linearly with increase in temperature, but remains relatively stable with enhanced precipitation. Our results also indicate that the differences in global patterns of CUE estimated by different approaches could be primarily resulted from their systematic differences in autotrophic respiration (Ra) rather than in GPP. Understanding mechanisms behind spatio-temporal changes in Ra is therefore a critical step towards better quantifying global CUE.