Jonas Ardö

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 simulateecosystem processes such as gross primary productivity (GPP). This study assesses theimpact of different widely used climate datasets on simulated gross primary productivity andevaluates the suitability of them for reproducing the global and regional carbon cycle asmapped from independent GPP data. We simulate GPP with the biogeochemical modelLPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP,PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-basedGPP product derived from eddy covariance measurements in combination with remotelysensed data. Our results show that all datasets tested produce relatively similar GPP simulationsat a global scale, corresponding fairly well to the observation-based data with a differencebetween simulations and observations ranging from -50 to 60 g m-2 yr-1. However, allsimulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1)and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwaveradiation for tropical areas was found to have the highest uncertainty in the analyzed historicalclimate datasets, we test whether simulation results could be improved by a correction ofthe tested shortwave radiation for tropical areas using a new radiation product from the InternationalSatellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) insimulated GPP magnitude was observed with bias corrected shortwave radiation, as well asan increase in spatio-temporal agreement between the simulated GPP and observationbasedGPP. This study conducts a spatial inter-comparison and quantification of the performancesof climate datasets and can thereby facilitate the selection of climate forcing dataover any given study area for modelling purposes.