A Bayesian calibration of a simple carbon cycle model : The role of observations in estimating and reducing uncertainty

@inproceedings{Ricciuto2008ABC,
  title={A Bayesian calibration of a simple carbon cycle model : The role of observations in estimating and reducing uncertainty},
  author={Daniel M. Ricciuto and Kenneth J. Davis and Klaus Keller},
  year={2008}
}
[1] The strengths of future carbon dioxide (CO2) sinks are highly uncertain. A sound methodology to characterize current and predictive uncertainties in carbon cycle models is crucial for the design of efficient carbon management strategies. We demonstrate such a methodology, Markov Chain Monte Carlo (MCMC), by performing a Bayesian calibration of a simple global-scale carbon cycle model with historical carbon cycle observations to (1) estimate probability density functions (PDFs) of key carbon… CONTINUE READING
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