To investigate global and regional gross primary productivity (GPP) and its sources of uncertainties, widely used model structures of light use efficiency models are integrated to build a meta-model. Meteorological reanalysis data and remote sensing data are combined to estimate GPP, and a systematical and quantitative uncertainty analysis is conducted based on the ANOVA approach. Results show that: 1) the meta-model results correspond well with the upscaling of eddy-covariance measurements (FLUXCOM) GPP with a Pearson correlation coefficient of 0.97 and root mean square error of 24.36 gC/(m²·month) and outperforms any single combination of model structure. 2) Photosynthetically active radiation (PAR), water-related data and water regulation scalar (Ws) are the three main sources of uncertainties for global GPP estimates, contributing 41.73%, 26.79% and 23.82% respectively to total variance. 3) Sources of uncertainties of regional GPP depend on environmental conditions. For arid areas, Ws is the dominant contributor (over 80%). In cold areas, temperature regulation scalar (Ts) introduces over 40% of uncertainty. The findings not only highlight the necessity to reduce uncertainty of PAR and water-related data to reduce uncertainty in global and regional GPP estimates, but also point out the importance of improving performances of Ws and Ts algorithms under extreme environmental conditions.

为探究全球及区域尺度总初级生产力(GPP)及其模型模拟的不确定性来源, 基于广泛使用的光能利用率模型的算法结构, 搭建多算法集成模型, 结合气象再分析数据和卫星遥感数据, 模拟全球及区域尺度总初级生产力, 并使用方差分析方法对模拟结果的不确定性来源进行量化研究。结果表明: 1) 集成模型与基于通量观测升尺度(FLUXCOM)的GPP之间具有较强的一致性, 皮尔逊相关系数达 0.97, 均方根误差为24.36 gC/(m²·月), 且集成模型的表现优于单一结构配置模型; 2) 光合有效辐射、水分相关数据及水分限制因子为不确定性的主要来源, 相对贡献分别为41.73%, 26.79%和23.82%; 3) 不确定性的构成具有明显的区域差异, 干旱区域水分限制因子的相对贡献超过80%, 低温区域温度限制因子的相对贡献超过40%。使用光能利用率模型估算GPP时, 控制光合有效辐射和水分相关数据的不确定性可以有效地提高模拟精度, 而在极端环境条件(干旱、低温)下, 优化环境条件限制因子至关重要。