Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models

摘要

[1] Continental to global-scale modeling of the carbon cycle using process-based models is subject to large uncertainties. These uncertainties originate from the model structure and uncertainty in model forcing fields; however, little is known about their relative importance. A thorough understanding and quantification of uncertainties is necessary to correctly interpret carbon cycle simulations and guide further model developments. This study elucidates the effects of different state-of-the-art land cover and meteorological data set options and biosphere models on simulations of gross primary productivity (GPP) over Europe. The analysis is based on (1) three different process-oriented terrestrial biosphere models (Biome-BGC, LPJ, and Orchidee) driven with the same input data and one model (Biome-BGC) driven with (2) two different meteorological data sets (ECMWF and REMO), (3) three different land cover data sets (GLC2000, MODIS, and SYNMAP), and (4) three different spatial resolutions of the land cover (0.25 degrees fractional, 0.25 degrees dominant, and 0.5 degrees dominant). We systematically investigate effects on the magnitude, spatial pattern, and interannual variation of GPP. While changing the land cover map or the spatial resolution has only little effect on the model outcomes, changing the meteorological drivers and especially the model results in substantial differences. Uncertainties of the meteorological forcings affect particularly strongly interannual variations of simulated GPP. By decomposing modeled GPP into their biophysical and ecophysiological components (absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), respectively) we show that differences of interannual GPP variations among models result primarily from differences of simulating RUE. Major discrepancies appear to be related to the feedback through the carbon-nitrogen interactions in one model (Biome-BGC) and water stress effects, besides the modeling of croplands. We suggest clarifying the role of nitrogen dynamics in future studies and revisiting currently applied concepts of carbon-water cycle interactions regarding the representation of canopy conductance and soil processes.