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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 generall...

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Autores principales: Guentchev, Galina S., Rood, Richard B., Ammann, Caspar M., Barsugli, Joseph J., Ebi, Kristie, Berrocal, Veronica, O’Neill, Marie S., Gronlund, Carina J., Vigh, Jonathan L., Koziol, Ben, Cinquini, Luca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808930/
https://www.ncbi.nlm.nih.gov/pubmed/26938544
http://dx.doi.org/10.3390/ijerph13030267
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author Guentchev, Galina S.
Rood, Richard B.
Ammann, Caspar M.
Barsugli, Joseph J.
Ebi, Kristie
Berrocal, Veronica
O’Neill, Marie S.
Gronlund, Carina J.
Vigh, Jonathan L.
Koziol, Ben
Cinquini, Luca
author_facet Guentchev, Galina S.
Rood, Richard B.
Ammann, Caspar M.
Barsugli, Joseph J.
Ebi, Kristie
Berrocal, Veronica
O’Neill, Marie S.
Gronlund, Carina J.
Vigh, Jonathan L.
Koziol, Ben
Cinquini, Luca
author_sort Guentchev, Galina S.
collection PubMed
description 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.
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spelling pubmed-48089302016-04-04 Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella Guentchev, Galina S. Rood, Richard B. Ammann, Caspar M. Barsugli, Joseph J. Ebi, Kristie Berrocal, Veronica O’Neill, Marie S. Gronlund, Carina J. Vigh, Jonathan L. Koziol, Ben Cinquini, Luca Int J Environ Res Public Health Article 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. MDPI 2016-02-29 2016-03 /pmc/articles/PMC4808930/ /pubmed/26938544 http://dx.doi.org/10.3390/ijerph13030267 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guentchev, Galina S.
Rood, Richard B.
Ammann, Caspar M.
Barsugli, Joseph J.
Ebi, Kristie
Berrocal, Veronica
O’Neill, Marie S.
Gronlund, Carina J.
Vigh, Jonathan L.
Koziol, Ben
Cinquini, Luca
Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title_full Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title_fullStr Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title_full_unstemmed Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title_short Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella
title_sort evaluating the appropriateness of downscaled climate information for projecting risks of salmonella
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4808930/
https://www.ncbi.nlm.nih.gov/pubmed/26938544
http://dx.doi.org/10.3390/ijerph13030267
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