Uncertainties of gross primary productivity of Chinese grasslands based on multi-source estimation - He, Panxing; Ma, Xiaoliang; Han, Zhiming; Meng, Xiaoyu; Sun, Zongjiu (Frontiers in Environmental Science, 2022)
Gross primary productivity (GPP) is an important parameter in the carbon cycle and climate change studies. The results of GPP fluxes estimated based on multiple models or remote sensing vary widely, but current studies of GPP in Chinese grasslands tend to directly ignore the uncertainty among data. In this study, uncertainty analysis of GPP datasets estimated based on terrestrial ecosystem models and remote sensing was conducted using cross-validation, standard error statistics and ensemble empirical modal decomposition. We found that (1) the fit coefficients R2 of two-by-two cross-validation of GPP datasets mostly exceeded 0.8 at the global scale. (2) GPP from different sources were consistent in portraying the spatial and temporal patterns of GPP in Chinese grasslands. However, due to many differences in model structure, parameterization and driving data, some uncertainties still exist, especially in the dry and cold parts of the region where the standard deviations are relatively large. (3) Uncertainties are higher for future scenarios than for historical periods, and GPP un-certainties are much higher for future high-emissions scenarios than for low- and me-dium-emissions scenarios. This study highlights the need for uncertainty analysis when GPP is applied to spatio-temporal analysis, and suggests that when comparing and as-sessing carbon balance conditions, multiple source data sets should be combined to avoid misleading conclusions due to uncertainty.