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

Abstract

[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°fractional, 0.25°d ominant, and 0.5°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. , et al. (2007), Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogeochem. Cycles, 21, GB4021,

Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants

“…Due to different physiological principles, underlying assumptions, and amounts of input data, GPP predictions vary, sometimes significantly (Coops et al, 2009 to evaluating model principles and uncertainties of driving forcing (Cramer et al, 1999;Ito and Sasai, 2006;Jung et al, 2007;Beer et al, 2010) a systematic inter-comparison project across available GPP models is warranted to achieve mechanistic interpretation of the disagreement of GPP predictions (Ryu et al, 2011). The evaluation of water stress factors at flux sites in this study shows that the definition of water stress factors could be the reason resulting in the significant difference of trend between different GPP products, which highlights the direction of GPP model improvements.…”

Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

“…Fourthly, in many regions the GPP interannual variability as simulated by LPJmL is controlled by variations of soil moisture (e.g. Jung et al, 2007;Weber et al, 2009). Soil moisture is a storage term and causes memory effects of the system, which is not taken into account by MTE.…”

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

“…Other studies have shown that the meteorological dataset used to drive LSMs is a large source of uncertainty in global land surface modelling (Hicke, 2005;Jung et al, 2007;. Different methods are used to create time series of global gridded climate data in order to drive LSMs, and this can introduce uncertainty that can propagate through model simulations (Zhao et al, 2006).…”