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

Abstract

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.

Different Satellite Products Revealing Variable Trends in Global Gross Primary Production

“…Both satellite‐derived vegetation indices and GPP indicated that vegetation exhibited a significant increasing trend before 2000, and in recent years the global vegetation growth rate had weakened due to climate change (Nemani et al., 2003; Pan et al., 2018; Yuan et al., 2019; Zhao & Running, 2010; Zhou et al., 2014). Although the overall trend in GPP has been increasing over the last few decades, there are some differences in the interannual variability (IAV) and estimates obtained using different GPP products (Anav et al., 2015; Yang et al., 2021). Meanwhile, the response of vegetation to climate change is complex, and there is some variation in IAV and long‐term trends of GPP (Ahlström, Raupach, et al., 2015; Cao et al., 2020).…”

The strengthened impact of water availability at interannual and decadal time scales on vegetation GPP

“…The GPP NIRv dataset was site-level verified, demonstrating a high correlation coefficient exceeding 0.7 for both monthly and annual variations in GPP. Different from light-use-efficiency (LUE) models, the trends of GPP NIRv are consistent with those from flux sites and process-based models (Yang, Wang, et al, 2022). Notably, the better abilities of GPP NIRv to capture the seasonal and interannual variations of terrestrial GPP are fundamental to detect the drivers of GPP variations and mechanisms of climate forces (Wang et al, 2021).…”

“…However, in ENF, EBF, DBF, and MF, we observed a weakening E WA-GPP (both positive and negative) for GPP NIRv , which is consistent with previous studies that there is a weakening impact of WA induced by carbon dioxide fertilization effects (Lian et al, 2023;Wang et al, 2020), whereas GPP FLUX and GPP EC-LUE exhibited an increasing E WA-GPP . Considering the better response of NIRv to high vegetation cover areas, we believe that the effect of WA on forests is weakening, which is more plausible, but of course, this needs to be further confirmed by other metrics such as biomass (Wang et al, 2021;Yang, Wang, et al, 2022).…”

Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model‐Data Fusion

“…Given the low sensitivity of these models to environmental scalars, this suggests that dynamic changes in MOD17 modeled GPP are largely a function of changes in canopy extent and vigor, conveyed by changes in fPAR. This feature of LUE models has been an advantage during the EOS era and allowed models like MOD17 to capture trends in the land carbon sink (Figure 5) that are otherwise missed by purely data‐driven approaches like FLUXCOM (Yang et al., 2022). And yet, given the modest improvement in the new MOD17 product compared to C61, it’s also apparent that the accuracy of these global LUE models is strongly tied to the quality of input data sets, in addition to uncertainty in model parameters and model structure.…”