Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

Oyebamiji, Oluwole Kehinde; Nemeth, Christopher; Harrison, Paula A. ORCID: https://orcid.org/0000-0002-9873-3338; Dunford, Robert W. ORCID: https://orcid.org/0000-0002-6559-1687; Cojocaru, George. 2023 Multivariate sensitivity analysis for a large-scale climate impact and adaptation model. Journal of the Royal Statistical Society Series C: Applied Statistics, 72 (3). 770-808. https://doi.org/10.1093/jrsssc/qlad032

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Abstract/Summary

We apply a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large data sets. The method was applied to multivariate data from the IMPRESSIONS Integrated Assessment Platform version 2. Our empirical results on Integrated Assessment Platform version 2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1093/jrsssc/qlad032
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-) Soils and Land Use (Science Area 2017-)
ISSN:	0035-9254
Additional Keywords:	Bayesian methods, compactly supported correlation function, Gaussian process, robust adaptive MCMC, sensitivity analysis
NORA Subject Terms:	Computer Science Data and Information
Related URLs:	Dataset
Date made live:	08 Nov 2023 10:04 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535678

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