Articles | Volume 11, issue 8
Methods for assessment of models
03 Aug 2018
Methods for assessment of models |  | 03 Aug 2018

Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output

Edmund Ryan, Oliver Wild, Apostolos Voulgarakis, and Lindsay Lee

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Cited articles

Ahtikoski, A., Heikkilä, J., Alenius, V., and Siren, M.: Economic viability of utilizing biomass energy from young stands – the case of Finland, Biomass Bioenerg., 32, 988–996, 2008. 
Ba, S., Myers, W. R., and Brenneman, W. A.: Optimal sliced Latin hypercube designs, Technometrics, 57, 479–487, 2015. 
Bailis, R., Ezzati, M., and Kammen, D. M.: Mortality and greenhouse gas impacts of biomass and petroleum energy futures in Africa, Science, 308, 98–103, 2005. 
Bastos, L. S. and O'Hagan, A.: Diagnostics for Gaussian process emulators, Technometrics, 51, 425–438, 2009. 
Campbell, J. E., Carmichael, G. R., Chai, T., Mena-Carrasco, M., Tang, Y., Blake, D., Blake, N., Vay, S. A., Collatz, G. J., and Baker, I.: Photosynthetic control of atmospheric carbonyl sulfide during the growing season, Science, 322, 1085–1088, 2008. 
Short summary
Global sensitivity analysis (GSA) identifies which parameters of a model most affect its output. We performed GSA using statistical emulators as surrogates of two slow-running atmospheric chemistry transport models. Due to the high dimension of the model outputs, we considered two alternative methods: one that reduced the output dimension and one that did not require an emulator. The alternative methods accurately performed the GSA but were significantly faster than the emulator-only method.