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Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?

Author

Listed:
  • Keith W. Dixon

    (NOAA Geophysical Fluid Dynamics Laboratory)

  • John R. Lanzante

    (NOAA Geophysical Fluid Dynamics Laboratory)

  • Mary Jo Nath

    (NOAA Geophysical Fluid Dynamics Laboratory)

  • Katharine Hayhoe

    (Texas Tech University)

  • Anne Stoner

    (Texas Tech University)

  • Aparna Radhakrishnan

    (Engility)

  • V. Balaji

    (Princeton University)

  • Carlos F. Gaitán

    (University of Oklahoma)

Abstract

Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:climat:v:135:y:2016:i:3:d:10.1007_s10584-016-1598-0
    DOI: 10.1007/s10584-016-1598-0
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    References listed on IDEAS

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    1. 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.
    2. Gaitan, Carlos F. & Cannon, Alex J., 2013. "Validation of historical and future statistically downscaled pseudo-observed surface wind speeds in terms of annual climate indices and daily variability," Renewable Energy, Elsevier, vol. 51(C), pages 489-496.
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    Cited by:

    1. Siabi, E. K. & Phuong, D. N. D. & Kabobah, A. T. & Akpoti, Komlavi & Anornu, G. & Incoom, A. B. M. & Nyantakyi, E. K. & Yeboah, K. A. & Siabi, S. E. & Vuu, C. & Domfeh, M. K. & Mortey, E. M. & Wemegah, 2023. "Projections and impact assessment of the local climate change conditions of the Black Volta Basin of Ghana based on the Statistical DownScaling Model," Papers published in Journals (Open Access), International Water Management Institute, pages 14(2):494-5.
    2. Guilong Li & Xuebin Zhang & Alex J. Cannon & Trevor Murdock & Steven Sobie & Francis Zwiers & Kevin Anderson & Budong Qian, 2018. "Indices of Canada’s future climate for general and agricultural adaptation applications," Climatic Change, Springer, vol. 148(1), pages 249-263, May.
    3. Galina S. Guentchev & Richard B. Rood & Caspar M. Ammann & Joseph J. Barsugli & Kristie Ebi & Veronica Berrocal & Marie S. O’Neill & Carina J. Gronlund & Jonathan L. Vigh & Ben Koziol & Luca Cinquini, 2016. "Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella," IJERPH, MDPI, vol. 13(3), pages 1-21, February.
    4. Bandi Aneesha Satya & Meshapam Shashi & Deva Pratap, 2019. "A geospatial approach to flash flood hazard mapping in the city of Warangal, Telangana, India," Environmental & Socio-economic Studies, Sciendo, vol. 7(3), pages 1-13, September.
    5. Poppick, Andrew & McKinnon, Karen A., 2020. "Observation-based Simulations of Humidity and Temperature Using Quantile Regression," Earth Arxiv bmskp, Center for Open Science.
    6. Carlos F. Gaitán, 2016. "Effects of variance adjustment techniques and time-invariant transfer functions on heat wave duration indices and other metrics derived from downscaled time-series. Study case: Montreal, Canada," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1661-1681, September.

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