Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Abstract. Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r  > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks.