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

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 and Luca Cinquini
Additional contact information
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

IJERPH, 2016, vol. 13, issue 3, 1-21

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.

Keywords: foodborne disease; Salmonella infections; evaluation; temperature-based heat indices; ARRM and BCCA statistical downscaling methods; Washington D.C. (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
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