Articles | Volume 13, issue 5
https://doi.org/10.5194/gmd-13-2487-2020
https://doi.org/10.5194/gmd-13-2487-2020
Methods for assessment of models
 | 
29 May 2020
Methods for assessment of models |  | 29 May 2020

Correcting a bias in a climate model with an augmented emulator

Doug McNeall, Jonny Williams, Richard Betts, Ben Booth, Peter Challenor, Peter Good, and Andy Wiltshire

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Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003. a
Andrianakis, I., Vernon, I. R., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M., and White, R. G.: Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda, PLoS Comput. Biol., 11, e1003968, https://doi.org/10.1371/journal.pcbi.1003968, 2015. a
Arendt, P. D., Apley, D. W., and Chen, W.: Quantification of model uncertainty: Calibration, model discrepancy, and identifiability, J. Mech. Design, 134, 100908, https://doi.org/10.1115/1.4007390, 2012a. a
Arendt, P. D., Apley, D. W., Chen, W., Lamb, D., and Gorsich, D.: Improving identifiability in model calibration using multiple responses, J. Mech. Design, 134, 100909, https://doi.org/10.1115/1.4007573, 2012b. a
Betts, R. A., Boucher O., Collins M., Cox, P. M., Falloon P. D., Gedney N., Hemming D. L., Huntingford C., Jones C. D., Sexton D. M., and Webb M. J.: Projected increase in continental runoff due to plant responses to increasing carbon dioxide, Nature, 448, 1037–1041, https://doi.org/10.1038/nature06045, 2007.  a
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Short summary
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land surface parameter settings than for other forests. It was unclear if this discrepancy was due to a bias in the modelled climate or an error in the land surface component of the model. Correcting the climate of the model with a statistical model corrects the simulation of the Amazon forest, suggesting that the land surface component of the model is not the source of the discrepancy.
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