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, due 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 an upscaled product from eddy covariance (EC) measurements, 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 extratropical Southern Hemisphere (Trop+SH) and extratropical 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 global total GPP was dominated by the CO2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance 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. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Background Air pollution in China has raised great concerns due to its adverse effects on air quality, human health, and climate. Emissions of air pollutants (APs) are inherently linked with CO2 emissions through fossil-energy consumption. Knowledge of the characteristics of APs and CO2 emissions and their relationships is fundamentally important in the pursuit of co-benefits in addressing air quality and climate issues in China. However, the linkages and interactions between APs and CO2 in China are not well understood. Results Here, we conducted an ensemble study of six bottom-up inventories to identify the underlying drivers of APs and CO2 emissions growth and to explore their linkages in China. The results showed that, during 1980–2015, the power and industry sectors contributed 61–79% to China’s overall emissions of CO2, NOx, and SO2. In addition, the residential and industrial sectors were large emitters (77–85%) of PM10, PM2.5, CO, BC, and OC. The emissions of CH4, N2O and NH3 were dominated by the agriculture sector (46–82%) during 1980–2015, while the share of CH4 emissions in the energy sector increased since 2010. During 1980–2015, APs and greenhouse gases (GHGs) emissions from residential sources generally decreased over time, while the transportation sector increased its impact on recent emissions, particularly for NOx and NMVOC. Since implementation of stringent pollution control measures and accompanying technological improvements in 2013, China has effectively limited pollution emissions (e.g., growth rates of –10% per year for PM and –20% for SO2) and slowed down the increasing trend of carbon emissions from the power and industrial sectors. We also found that areas with high emissions of CO, NOx, NMVOC, and SO2 also emitted large amounts of CO2, which demonstrates the possible common sources of APs and GHGs. Moreover, we found significant correlations between CO2 and APs (e.g., NOx, CO, SO2, and PM) emissions in the top 5% high-emitting grid cells, with more than 60% common grid cells during 2010–2015. Conclusions We found significant correlation in spatial and temporal aspects for CO2, and NOx, CO, SO2, and PM emissions in China. We targeted sectorial and spatial APs and GHGs emission hot-spots, which help for management and policy-making of collaborative reductions of them. This comprehensive analysis over 6 datasets improves our understanding of APs and GHGs emissions in China during the period of rapid industrialization from 1980 to 2015. This study helps elucidate the linkages between APs and CO2 from an integrated perspective, and provides insights for future synergistic emissions reduction.