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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 7
Geosci. Model Dev., 8, 1899–1918, 2015
https://doi.org/10.5194/gmd-8-1899-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 1899–1918, 2015
https://doi.org/10.5194/gmd-8-1899-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 01 Jul 2015

Methods for assessment of models | 01 Jul 2015

Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model

C. Safta1, D. M. Ricciuto2, K. Sargsyan1, B. Debusschere1, H. N. Najm1, M. Williams3, and P. E. Thornton2 C. Safta et al.
  • 1Sandia National Labs, Livermore, CA 94551, USA
  • 2Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
  • 3School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, EH9 EJN, UK

Abstract. In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.

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In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
In this paper we propose a probabilistic framework for an uncertainty quantification study of a...
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