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Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

Author

Listed:
  • Seshagiri Rao Kolusu

    (Met Office
    University of Sussex)

  • Christian Siderius

    (London School of Economics
    Uncharted Waters Research)

  • Martin C. Todd

    (University of Sussex)

  • Ajay Bhave

    (Newcastle University)

  • Declan Conway

    (London School of Economics)

  • Rachel James

    (Oxford University Centre for the Environment)

  • Richard Washington

    (Oxford University Centre for the Environment)

  • Robel Geressu

    (University of Manchester)

  • Julien J. Harou

    (University of Manchester
    University College London)

  • Japhet J. Kashaigili

    (Sokoine University of Agriculture)

Abstract

Uncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

Suggested Citation

  • Seshagiri Rao Kolusu & Christian Siderius & Martin C. Todd & Ajay Bhave & Declan Conway & Rachel James & Richard Washington & Robel Geressu & Julien J. Harou & Japhet J. Kashaigili, 2021. "Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa," Climatic Change, Springer, vol. 164(3), pages 1-20, February.
    • Handle: RePEc:spr:climat:v:164:y:2021:i:3:d:10.1007_s10584-021-02991-8
    DOI: 10.1007/s10584-021-02991-8
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    References listed on IDEAS

    as
    1. David Rowell & Catherine Senior & Michael Vellinga & Richard Graham, 2016. "Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance?," Climatic Change, Springer, vol. 134(4), pages 621-633, February.
    2. Declan Conway & Robert J. Nicholls & Sally Brown & Mark G. L. Tebboth & William Neil Adger & Bashir Ahmad & Hester Biemans & Florence Crick & Arthur F. Lutz & Ricardo Safra Campos & Mohammed Said & Ch, 2019. "The need for bottom-up assessments of climate risks and adaptation in climate-sensitive regions," Nature Climate Change, Nature, vol. 9(7), pages 503-511, July.
    3. Hurford, A.P. & Harou, J.J. & Bonzanigo, L. & Ray, P.A. & Karki, P. & Bharati, L. & Chinnasamy, P., 2020. "Efficient and robust hydropower system design under uncertainty - A demonstration in Nepal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    4. David P. Rowell & Catherine A. Senior & Michael Vellinga & Richard J. Graham, 2016. "Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance?," Climatic Change, Springer, vol. 134(4), pages 621-633, February.
    5. Patrick A. Ray & Casey M. Brown, 2015. "Confronting Climate Uncertainty in Water Resources Planning and Project Design," World Bank Publications - Books, The World Bank Group, number 22544, December.
    6. Christoph Baumberger & Reto Knutti & Gertrude Hirsch Hadorn, 2017. "Building confidence in climate model projections: an analysis of inferences from fit," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 8(3), May.
    7. Alistair Hunt & Paul Watkiss, 2011. "Climate change impacts and adaptation in cities: a review of the literature," Climatic Change, Springer, vol. 104(1), pages 13-49, January.
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    Cited by:

    1. Siderius, C. & Biemans, H. & Kashaigili, J. & Conway, D., 2022. "Water conservation can reduce future water-energy-food-environment trade-offs in a medium-sized African river basin," Agricultural Water Management, Elsevier, vol. 266(C).
    2. Ma, Y. & Li, Y.P. & Huang, G.H., 2023. "Planning China’s non-deterministic energy system (2021–2060) to achieve carbon neutrality," Applied Energy, Elsevier, vol. 334(C).

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