Selection of likelihood parameters for complex models determines the effectiveness of Bayesian calibration

Assessing the parameter uncertainty of complex ecosystem models is a key challenge for improving our understanding of real world abstractions, such as those for explaining carbon and nitrogen cycle at ecosystem scale and associated biosphere-atmosphere-hydrosphere exchange processes. The lack of data about the variance of measurements forces scientists to revisit assumptions used in estimating the parameter distribution of complex ecosystem models. An increasingly used tool for assessing parameter uncertainty of complex ecosystem models is Bayesian calibration. In this paper, we generate two data sets which may represent a seasonal temperature curve or the seasonality of soil carbon dioxide flux and a single high peak put on a low background signal as is e.g. typical for soil nitrous oxide emission. Based on these examples we illustrate that commonly used assumptions for measurement uncertainty can lead to a sampling of wrong areas in the parameter space, incorrect parameter dependencies, and an underestimation of parameter uncertainties. This step needs particular attention by modelers as these issues lead to erroneous model simulations a) in present and future domains, b) misinterpretations of process feedback and functioning of the model, and c) to an underestimation of model uncertainty (e.g. for soil greenhouse gas fluxes). We also test the extension of the Bayesian framework with a model error term to compensate the effects caused by the false assumption of a perfect model and show that this approach can alleviate the observed problems in estimating the model parameter distribution. (C) 2011 Elsevier B.V. All rights reserved.