Bayesian inversions of a dynamic vegetation model at four European grassland sites

Eddy covariance data from four European grassland sites are used to probabilistically calibrate of the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model applied to the simulation of daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET). A focus is made on comparing model inversions, considering both homoscedasticand heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modeled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals.