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Abstract
Parkinson, S. and Young, P.C. (1998) Uncertainty and sensitivity in global carbon cycle modelling, Climate Research, 9, 157-174.
| This paper summarises the results obtained from a stochastic sensitivity study in the area of global carbon cycle modelling. In particular, it outlines how Monte Carlo Simulation (MCS) techniques and associated Generalised Sensitivity Analysis (GSA) have been applied to the modified box-diffusion model of Enting and Lassey (1993). Using parametric and input uncertainty measures derived from independent sources, the analysis shows that the model produces an ensemble response for atmospheric whose mean evolution is reasonably consistent with that observed over the industrial period (1765-1990). However, the situation is more ambiguous in relation to the amplitude distribution of the stochastic realisations: here, there is a larger than expected variance arising from the assumed uncertainty, although this may be due to unavoidable limitations in the assumptions about parameteric uncertainty used in the MCS analysis. The analysis also shows that the a priori model response is less consistent with measurements and not at all consistent with the observations; while GSA suggests that, from over twenty model parameters, only a small number have a statistically significant effect on the model response in 1990. In addition, the results from MCS, using the IPCC Scenario IS92a inputs, demonstrate that the uncertainties in the projections of the concentrations using this model are significantly higher than those suggested so far using deterministic methods. Finally, the advantages and disadvantages of the stochastic methodology are discussed. |
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Updating responsibility Arun Chotai. This page is copyright of Lancaster University.
12/10/01 - PGM.