Articles | Volume 7, issue 4
Earth Syst. Dynam., 7, 917–935, 2016
Earth Syst. Dynam., 7, 917–935, 2016
Research article
24 Nov 2016
Research article | 24 Nov 2016

The impact of structural error on parameter constraint in a climate model

Doug McNeall et al.

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Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827,, 2012.
Booth, B. B. B., Jones, C. D., Collins, M., Totterdell, I. J., Cox, P. M., Sitch, S., Huntingford, C., Betts, R. A., Harris, G. R., and Lloyd, J.: High sensitivity of future global warming to land carbon cycle processes, Environ. Res. Lett., 7, 024002,, 2012.
Booth, B. B. B., Bernie, D., McNeall, D., Hawkins, E., Caesar, J., Boulton, C., Friedlingstein, P., and Sexton, D. M. H.: Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models, Earth Syst. Dynam., 4, 95–108,, 2013.
Bounceur, N., Crucifix, M., and Wilkinson, R. D.: Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach, Earth Syst. Dynam., 6, 205–224,, 2015.
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters: the importance of model discrepancy, Inverse Problems, 30, 114007,, 2014.
Short summary
We compare simulated with observed forests to constrain uncertain input parameters of the land surface component of a climate model. We find that the model is unlikely to be able to simulate the Amazon and other major forests simultaneously at any one parameter set, suggesting a bias in the model's representation of the Amazon. We find we cannot constrain parameters individually, but we can rule out large areas of joint parameter space.
Final-revised paper