The potential of an observational data set for calibration of a computationally expensive computer model

Geoscientific Model Development

Volumn 6, Issue 5, 2013, Pages 1715-1728

  Mcneall, D J ^a   Challenor, P G ^b   Gattiker, J R ^c   Stone, E J ^d  

^a MET OFFICE   (United Kingdom)

^b UNIVERSITY OF EXETER   (United Kingdom)

^c LOS ALAMOS NATIONAL LABORATORY   (United States)

^d UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; COMPUTER SIMULATION; DATA SET; EFFICIENCY MEASUREMENT; MODEL VALIDATION; PARAMETERIZATION;

EID: 84886788340     PISSN: 1991959X     EISSN: 19919603     Source Type: Journal    
DOI: 10.5194/gmd-6-1715-2013     Document Type: Article

References (24)

1. Bamber, J., Layberry, R. L., and Gogineni, P.: A new ice thickness and bed data set for the Greenland ice sheet 1. Measurement, data reduction, and errors, J. Geophys. Res., 106, 33-773, 2001.
2. Craig, P., Goldstein, M., Seheult, A., and Smith, J.: Bayes linear strategies for history matching of hydrocarbon reservoirs, in: Bayesian Statistics 5, edited by: Bernardo, J., Berger, J., Dawid, A., and Smith, A., 69-95, Clarendon Press, Oxford, UK, 1996.
3. Craig, P., Goldstein, M., Seheult, A., and Smith, J.: Pressure matching for hydrocarbon reservoirs: a case study in the use of Bayes linear strategies for large computer experiments, in: Case studies in Bayesian statistics, edited by: Gatsonis, C., Hodges, J., Kass, R., McCulloch, R., Rossi, P., and Singpurwalla, N., vol. 3, 36-93, Springer-Verlag, New York, USA, 1997.
4. Craig, P., Goldstein, M., Rougier, J., and Seheult, A.: Bayesian forecasting for complex systems using computer simulators, J. Am. Stat. Assoc., 96, 717-729, 2001.
5. Hankin, R.: Introducing BACCO, an R package for Bayesian analysis of computer code output, J. Stat. Softw., 14, 1-21, 2005.
6. Kennedy, M. and O'Hagan, A.: Bayesian calibration of computer models, J. Roy. Stat. Soc.: Series B (Statistical Methodology), 63, 425-464, 2001.
7. McKay, M., Beckman, R., and Conover, W.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21, 239-245, 1979. (Pubitemid 9452235)
8. Oakley, J.: Bayesian uncertainty analysis for complex computer codes, Ph.D. thesis, University of Sheffield, Sheffield, UK, 1999.
9. Oakley, J. and O'Hagan, A.: Probabilistic sensitivity analysis of complex models: a Bayesian approach, J. Roy. Stat. Soc.: Series B (Statistical Methodology), 66, 751-769, 2004. (Pubitemid 39015911)
10. Partridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Struthers, H., and Sorooshian, A.: Inverse modelling of cloudaerosol interactions-Part 2: Sensitivity tests on liquid phase clouds using a Markov chain Monte Carlo based simulation approach, Atmos. Chem. Phys., 12, 2823-2847, doi:10.5194/acp-12-2823- 2012, 2012.
11. Payne, A.: A thermomechanical model of ice flow in West Antarctica, Clim. Dynam., 15, 115-125, 1999. (Pubitemid 29221290)
12. Pukelsheim, F.: The three sigma rule, American Statistician, 48, 88-91, 1994.
13. R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org, ISBN 3-900051-07-0, 2012.
14. Rougier, J.: Probabilistic inference for future climate using an ensemble of climate model evaluations, Climatic Change, 81, 247-264, 2007. (Pubitemid 46363540)
15. Rutt, I., Hagdorn, M., Hulton, N., and Payne, A.: The Glimmer community ice sheet model, J. Geophys. Res., 114, F02004, doi:10.1029/2008JF001015, 2009.
16. Saltelli, A., Chan, K. and Scott, E. (Eds.): Sensitivity analysis, vol. 134, Wiley New York, 2000.
17. Santner, T., Williams, B., and Notz, W.: The design and analysis of computer experiments, Springer, 2003.
18. Sexton, D. and Murphy, J.: Multivariate probabilistic projections using imperfect climate models. Part II: robustness of methodological choices and consequences for climate sensitivity, Clim. Dynam., 38, 1-16, 2543-2558, doi:10.1007/s0038201112098, 2012.
19. Sexton, D., Murphy, J., Collins, M., and Webb, M.: Multivariate probabilistic projections using imperfect climate models part I: outline of methodology, Clim. Dynam., 38, 2513-2542, doi:10.1007/s0038201112089, 2012.
20. Stone, E. J., Lunt, D. J., Rutt, I. C., and Hanna, E.: Investigating the sensitivity of numerical model simulations of the modern state of the Greenland ice-sheet and its future response to climate change, The Cryosphere, 4, 397-417, doi:10.5194/tc-4-397-2010, 2010.
21. Tebaldi, C. and Knutti, R.: The use of the multi-model ensemble in probabilistic climate projections, Philosophical Transactions of the Royal Society A: Mathematical, Phys. Eng. Sci., 365, 2053-2075, 2007.
22. Vernon, I., Goldstein, M., and Bower, R.: Galaxy formation: a Bayesian uncertainty analysis, Bayesian Analysis, 5, 619-669, 2010.
23. Wilkinson, R.: Bayesian calibration of expensive multivariate computer experiments, Large-Scale Inverse Problems and Quantification of Uncertainty, 195-215, 2011.
24. Ziehn, T., Scholze, M., and Knorr, W.: On the capability of Monte Carlo and adjoint inversion techniques to derive posterior parameter uncertainties in terrestrial ecosystem models, Global Biogeochem. Cy., 26, GB3025, doi:10.1029/2011GB004185, 2012.