Uncertainty analysis of multiple terrestrial gross primary productivity products

Abstract Aim Gross primary productivity (GPP) of vegetation is an important component of the carbon balance of terrestrial ecosystems. With the development of science and technology, there has been a proliferation of multiple GPP products, yet large uncertainties exist in the global GPP estimated by different GPP products. The objectives of this study were to assess the uncertainty of 45 GPP products in 4 different categories (machine learning, Fluxnet, light use efficiency, land surface model) and to perform an attribution analysis. Location Global. Time period 2003–2010. Major taxa studied Terrestrial ecosystems. Methods The three‐cornered hat (TCH) method was used to assess GPP uncertainty because it does not require a priori knowledge of the true GPP values. We also used generalized additive models to explore the contribution of different factors to GPP uncertainty. Results The results show that the magnitude of absolute uncertainty (AU) is generally higher in the GPP high‐value region and also indicate large spatial variability of relative uncertainty (RU) among products. Deciduous broadleaf forest (20.2 g C/m 2 /year) and deciduous needleleaf forest (27.2 g C/m 2 / year) have lower uncertainty compared to that of woody savanna (128.4 g C/m 2 / year) and savanna (128.7 g C/m 2 /year). The RU in machine learning (ML) products overall is low (average value = 14.3%), with ORCHIDEE‐CNP (weight = 4.19%), being more recognized by GPP products are more consistent in the magnitude of spatial variability (similar in the standard deviations) and more uncertain in spatial distribution patterns. Excluding the combined effects of other factors, soil moisture (15.2%) and precipitation (8.3%) are the two factors that contribute most to GPP uncertainty, indicating that focusing on water constraints is a guarantee of GPP product quality. Main conclusions The TCH approach quantifies the uncertainty of GPP products well and the spatial distribution pattern variability contributes more uncertainty compared to the magnitude of spatial variability. Future GPP estimates could consider combining the advantages of different GPP products to limit uncertainty.