Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/bg-2017-41
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
22 Feb 2017
Review status
A revision of this discussion paper is under review for the journal Biogeosciences (BG).
Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods
Dan Lu1, Daniel Ricciuto2, Anthony Walker2, Cosmin Safta3, and William Munger4 1Computer Science and Mathematics Division, Climate Change Science Institute, Oak Ridge 7 National Laboratory, Oak Ridge, TN, USA
2Environmental Sciences Division, Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA
3Sandia National Laboratories, Livermore, CA, USA
4School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chain method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. It reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.

Citation: Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods, Biogeosciences Discuss., https://doi.org/10.5194/bg-2017-41, in review, 2017.
Dan Lu et al.
Dan Lu et al.
Dan Lu et al.

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