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
National Climate Predictions and Projections platform (NCPP), NCAR RAL CSAP, 3450 Mitchell Lane, Boulder, CO 80301, USA.
Department Atmospheric, Oceanic and Space Sciences, University of Michigan, 525 Space Research Building, Ann Arbor, MI 48109-2143, USA.
Int J Environ Res Public Health. 2016 Feb 29;13(3):267. doi: 10.3390/ijerph13030267.
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.
食源性疾病在全球范围内具有重大的经济和社会影响。为了评估食源性疾病风险可能如何因气候变化而改变，需要针对特定应用问题量身定制可靠且可用的气候信息。全球气候模型（GCM）数据通常既需要降尺度到适用的尺度才能使用，又要很好地体现造成健康影响的关键特征。本研究对华盛顿特区地区基于温度的热指数进行了评估，这些指数源自1971 - 2000年统计降尺度的GCM模拟——这是确立这些数据可信度的必要步骤。这些指数近似于先前与沙门氏菌感染发生率相关的高周平均温度。由于异步区域回归模型（ARRM）和偏差校正构造相似法（BCCA）降尺度方法中包含偏差校正，热指数的观测30年平均值得到了较好的再现。然而，在4月和5月，一些统计降尺度数据误报了临近夏季时炎热天数的增加情况。本研究证明了结果对降尺度气候数据选择的依赖性以及对未来沙门氏菌感染估计进行错误解读的可能性。
