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Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

Author

Listed:
  • Galina S. Guentchev

    (National Climate Predictions and Projections platform (NCPP), NCAR RAL CSAP, 3450 Mitchell Lane, Boulder, CO 80301, USA)

  • Richard B. Rood

    (Department Atmospheric, Oceanic and Space Sciences, University of Michigan, 525 Space Research Building, Ann Arbor, MI 48109-2143, USA)

  • Caspar M. Ammann

    (National Climate Predictions and Projections platform (NCPP), NCAR RAL CSAP, 3450 Mitchell Lane, Boulder, CO 80301, USA)

  • Joseph J. Barsugli

    (CIRES—NOAA/University of Colorado, 325 Broadway, Boulder, CO 80305-3328, USA)

  • Kristie Ebi

    (Department of Global Health, School of Public Health, University of Washington, 1959 NE Pacific Street, Health Sciences Building, Seattle, WA 98195, USA)

  • Veronica Berrocal

    (Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA)

  • Marie S. O’Neill

    (Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA)

  • Carina J. Gronlund

    (Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA)

  • Jonathan L. Vigh

    (NCAR JNT RAL, 3450 Mitchell Lane, Boulder, CO 80301, USA)

  • Ben Koziol

    (CIRES—NOAA/University of Colorado, 325 Broadway, Boulder, CO 80305-3328, USA)

  • Luca Cinquini

    (NESII—NOAA/ESRL, 325 Broadway, Boulder, CO 80305-3328, USA)

Abstract

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:3:p:267-:d:64714
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    References listed on IDEAS

    as
    1. 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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