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

TL;DR

This paper introduces an efficient Bayesian global sensitivity analysis method for large-scale, multivariate climate impact models, utilizing Gaussian process surrogates, sparse matrices, and parallel algorithms to improve computational performance.

Contribution

It presents a novel combination of sparse Gaussian process surrogates and adaptive algorithms for sensitivity analysis of complex, high-dimensional climate models.

Findings

Method is efficient and accurate on synthetic data.

Applicable to large-scale climate impact datasets.

Reduces computational cost significantly.

Abstract

We develop a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. The focus is on computationally demanding models with correlated variables. 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 datasets, where we use cross-validation to determine the optimal degree of sparsity. This method was combined with a robust adaptive Metropolis algorithm coupled with a parallel implementation to speed up the convergence to the target distribution. The method was applied to a multivariate dataset from the IMPRESSIONS Integrated Assessment Platform (IAP2), an extension of the CLIMSAVE IAP, which has been widely applied in climate change impact, adaptation and vulnerability assessments. Our empirical results on synthetic and IAP2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.