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Interrogating empirical-statistical downscaling

Author

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  • B. Hewitson
  • J. Daron
  • R. Crane
  • M. Zermoglio
  • C. Jack

Abstract

The delivery of downscaled climate information is increasingly seen as a vehicle of climate services, a driver for impacts studies and adaptation decisions, and for informing policy development. Empirical-statistical downscaling (ESD) is widely used; however, the accompanying responsibility is significant, and predicated on effective understanding of the limitations and capabilities of ESD methods. There remain substantial contradictions, uncertainties, and sensitivity to assumptions between the different methods commonly used. Yet providing decision-relevant downscaled climate projections to help support national and local adaptation is core to the growing global momentum seeking to operationalize what is, in effect, still foundational research. We argue that any downscaled climate information must address the criteria of being plausible, defensible and actionable. Climate scientists cannot absolve themselves of their ethical responsibility when informing adaptation and must, therefore, be diligent in ensuring any information provided adequately addresses these three criteria. Frameworks for supporting such assessment are not well developed. We interrogate the conceptual foundations of statistical downscaling methodologies and their assumptions, and articulate a framework for evaluating and integrating downscaling output into the wider landscape of climate information. For ESD there are key criteria that need to be satisfied to underpin the credibility of the derived product. Assessing these criteria requires the use of appropriate metrics to test the comprehensive treatment of local climate response to large-scale forcing, and to compare across methods. We illustrate the potential consequences of methodological choices on the interpretation of downscaling results and explore the purposes, benefits and limitations of using statistical downscaling. Copyright The Author(s) 2014

Suggested Citation

  • B. Hewitson & J. Daron & R. Crane & M. Zermoglio & C. Jack, 2014. "Interrogating empirical-statistical downscaling," Climatic Change, Springer, vol. 122(4), pages 539-554, February.
  • Handle: RePEc:spr:climat:v:122:y:2014:i:4:p:539-554
    DOI: 10.1007/s10584-013-1021-z
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    References listed on IDEAS

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    1. D. Jeong & A. St-Hilaire & T. Ouarda & P. Gachon, 2012. "Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator," Climatic Change, Springer, vol. 114(3), pages 567-591, October.
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    4. J. Bedia & S. Herrera & D. Martín & N. Koutsias & J. Gutiérrez, 2013. "Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling," Climatic Change, Springer, vol. 120(1), pages 229-247, September.
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    Cited by:

    1. Joseph Daron & Ian Macadam & Hideki Kanamaru & Thelma Cinco & Jack Katzfey & Claire Scannell & Richard Jones & Marcelino Villafuerte & Faye Cruz & Gemma Narisma & Rafaela Jane Delfino & Rodel Lasco & , 2018. "Providing future climate projections using multiple models and methods: insights from the Philippines," Climatic Change, Springer, vol. 148(1), pages 187-203, May.
    2. Ye, Bin & Jiang, Jingjing & Liu, Junguo & Zheng, Yi & Zhou, Nan, 2021. "Research on quantitative assessment of climate change risk at an urban scale: Review of recent progress and outlook of future direction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    3. R. Manzanas & L. Fiwa & C. Vanya & H. Kanamaru & J. M. Gutiérrez, 2020. "Statistical downscaling or bias adjustment? A case study involving implausible climate change projections of precipitation in Malawi," Climatic Change, Springer, vol. 162(3), pages 1437-1453, October.
    4. Keith W. Dixon & John R. Lanzante & Mary Jo Nath & Katharine Hayhoe & Anne Stoner & Aparna Radhakrishnan & V. Balaji & Carlos F. Gaitán, 2016. "Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?," Climatic Change, Springer, vol. 135(3), pages 395-408, April.

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