Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r