Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.
Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.
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DOI：
10.3390/ijerph13030267
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发表时间：
2016-02-29
影响因子：
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通讯作者：
Cinquini L
中科院分区：
文献类型：
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作者：
Guentchev GS;Rood RB;Ammann CM;Barsugli JJ;Ebi K;Berrocal V;O'Neill MS;Gronlund CJ;Vigh JL;Koziol B;Cinquini L
食源性疾病在世界范围内具有巨大的经济和社会影响。为了评估食源性疾病的风险如何随着气候变化而变化，需要针对具体应用问题提供可信和可用的气候信息。全球气候模型（GCM）数据通常需要缩小到应用程序的规模才能使用，并代表造成健康影响的关键特征。这项研究提出了一个基于温度的热指数为华盛顿地区的评估来自1971年至2000年的统计缩减GCM模拟-在建立这些数据的可信度的必要步骤。这些指数近似于以前与沙门氏菌感染发生有关的每周平均高温。由于偏差校正，包括在异步区域回归模型（ARRM）和偏差校正构造类比（BCCA）降尺度方法，观察到的30年平均值的热量指数再现合理。然而，在4月和5月，一些统计上缩小的数据错误地反映了夏季炎热天数的增加。这项研究表明，结果的依赖性，以选择缩小规模的气候数据和误解的沙门氏菌感染的未来估计的潜力。
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.