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Understanding the dynamics and evolution of climate change and associateduncertainties is key for designing robust policy actions. Computer models arekey tools in this scientific effort, which have now reached a high level ofsophistication and complexity. Model auditing is needed in order to betterunderstand their results, and to deal with the fact that such models areincreasingly opaque with respect to their inner workings. Current techniquessuch as Global Sensitivity Analysis (GSA) are limited to dealing either withmultivariate outputs, stochastic ones, or finite-change inputs. This limitstheir applicability to time-varying variables such as future pathways ofgreenhouse gases. To provide additional semantics in the analysis of a modelensemble, we provide an extension of GSA methodologies tackling the case ofstochastic functional outputs with finite change inputs. To deal with finitechange inputs and functional outputs, we propose an extension of currentlyavailable GSA methodologies while we deal with the stochastic part byintroducing a novel, domain-selective inferential technique for sensitivityindices. Our method is explored via a simulation study that shows itsrobustness and efficacy in detecting sensitivity patterns. We apply it to realworld data, where its capabilities can provide to practitioners andpolicymakers additional information about the time dynamics of sensitivitypatterns, as well as information about robustness.

Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models