Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
Copyright policy )Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
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.
- University of Mary United States
- National Oceanic and Atmospheric Administration United States
- Department of Epidemiology, School of Public Health, University of Washington United States
- University of Colorado System United States
- University of Colorado Boulder United States
Washington, Hot Temperature, 550, Salmonella infections, Climate, Climate Change, 910, Toxicology, Article, Theoretical, Models, Risk Factors, Salmonella, Washington D.C., Humans, foodborne disease; <i>Salmonella</i> infections; evaluation; temperature-based heat indices; ARRM and BCCA statistical downscaling methods; Washington D.C., evaluation, Models, Theoretical, foodborne disease, Climate Action, Washington D.C, Salmonella Infections, ARRM and BCCA statistical downscaling methods, temperature-based heat indices, Seasons, Environmental Monitoring
Washington, Hot Temperature, 550, Salmonella infections, Climate, Climate Change, 910, Toxicology, Article, Theoretical, Models, Risk Factors, Salmonella, Washington D.C., Humans, foodborne disease; <i>Salmonella</i> infections; evaluation; temperature-based heat indices; ARRM and BCCA statistical downscaling methods; Washington D.C., evaluation, Models, Theoretical, foodborne disease, Climate Action, Washington D.C, Salmonella Infections, ARRM and BCCA statistical downscaling methods, temperature-based heat indices, Seasons, Environmental Monitoring
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