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What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling

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  • Wagener, Thorsten

    (University of Bristol)

  • Pianosi, Francesca

Abstract

Computer models are essential tools in the earth system sciences. They underpin our search for understanding of earth system functioning and support decision- and policy-making across spatial and temporal scales. To understand the implications of uncertainty and environmental variability on the identification of such earth system models and their predictions, we can rely on increasingly powerful Global Sensitivity Analysis (GSA) methods. Previous reviews have characterised the variability of GSA methods available and their usability for different tasks. In our paper we rather focus on reviewing what has been learned so far by applying GSA to models across the earth system sciences, independently of the specific algorithm that was applied. We identify and discuss 10 key findings with general applicability and relevance for the earth sciences. We further provide an A-B-C-D of best practise in applying GSA methods, which we have derived from analysing why some GSA applications provided more insight than others.

Suggested Citation

  • Wagener, Thorsten & Pianosi, Francesca, 2019. "What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling," Earth Arxiv g9ma5, Center for Open Science.
    • Handle: RePEc:osf:eartha:g9ma5
    DOI: 10.31219/osf.io/g9ma5
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    References listed on IDEAS

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    1. Marco Ratto, 2008. "Analysing DSGE Models with Global Sensitivity Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 31(2), pages 115-139, March.
    2. Declan Butler, 2007. "Earth Monitoring: The planetary panopticon," Nature, Nature, vol. 450(7171), pages 778-780, December.
    3. Paleari, Livia & Confalonieri, Roberto, 2016. "Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions," Ecological Modelling, Elsevier, vol. 340(C), pages 57-63.
    4. Ben Touhami, Haythem & Lardy, Romain & Barra, Vincent & Bellocchi, Gianni, 2013. "Screening parameters in the Pasture Simulation model using the Morris method," Ecological Modelling, Elsevier, vol. 266(C), pages 42-57.
    5. Song, Xiaodong & Bryan, Brett A. & Paul, Keryn I. & Zhao, Gang, 2012. "Variance-based sensitivity analysis of a forest growth model," Ecological Modelling, Elsevier, vol. 247(C), pages 135-143.
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    Cited by:

    1. Connor, Jeffery D. & Summers, David & Regan, Courtney & Abbott, Hayley & Van Der Linden, Leon & Frizenschaf, Jacqueline, 2022. "Sensitivity analysis in economic evaluation of payments for water and carbon ecosystem services," Ecosystem Services, Elsevier, vol. 54(C).
    2. Chuan Qin & Yuqing Jin & Meng Tian & Ping Ju & Shun Zhou, 2023. "Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification," Energies, MDPI, vol. 16(16), pages 1-21, August.
    3. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.

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