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.

A decision tree algorithm for investigation of model biases related to dynamical cores and physical parameterizations.

An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.

Using forecast and observed weather data to assess performance of forecast products in identifying heat waves and estimating heat wave effects on mortality.

BACKGROUND: Heat wave and health warning systems are activated based on forecasts of health-threatening hot weather. OBJECTIVE: We estimated heat-mortality associations based on forecast and observed weather data in Detroit, Michigan, and compared the accuracy of forecast products for predicting heat waves. METHODS: We derived and compared apparent temperature (AT) and heat wave days (with heat waves defined as ≥ 2 days of daily mean AT ≥ 95th percentile of warm-season average) from weather observations and six different forecast products. We used Poisson regression with and without adjustment for ozone and/or PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) to estimate and compare associations of daily all-cause mortality with observed and predicted AT and heat wave days. RESULTS: The 1-day-ahead forecast of a local operational product, Revised Digital Forecast, had about half the number of false positives compared with all other forecasts. On average, controlling for heat waves, days with observed AT = 25.3°C were associated with 3.5% higher mortality (95% CI: -1.6, 8.8%) than days with AT = 8.5°C. Observed heat wave days were associated with 6.2% higher mortality (95% CI: -0.4, 13.2%) than non-heat wave days. The accuracy of predictions varied, but associations between mortality and forecast heat generally tended to overestimate heat effects, whereas associations with forecast heat waves tended to underestimate heat wave effects, relative to associations based on observed weather metrics. CONCLUSIONS: Our findings suggest that incorporating knowledge of local conditions may improve the accuracy of predictions used to activate heat wave and health warning systems.

Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems.

Heat waves have been linked to excess mortality and morbidity, and are projected to increase in frequency and intensity with a warming climate. This study compares exposure metrics to trigger heat wave and health warning systems (HHWS), and introduces a novel multi-level hybrid clustering method to identify potential dangerously hot days. Two-level and three-level hybrid clustering analysis as well as common indices used to trigger HHWS, including spatial synoptic classification (SSC), and the 90th, 95th, and 99th percentiles of minimum and relative minimum temperature (using a 10 day reference period), were calculated using a summertime weather dataset in Detroit from 1976 to 2006. The days classified as 'hot' with hybrid clustering analysis, SSC, minimum and relative minimum temperature methods differed by method type. SSC tended to include the days with, on average, 2.5 °C lower daily minimum temperature and 5.3 °C lower dew point than days identified by other methods. These metrics were evaluated by comparing their performance in predicting excess daily mortality. The 99th percentile of minimum temperature was generally the most predictive, followed by the three-level hybrid clustering method, the 95th percentile of minimum temperature, SSC and others. Our proposed clustering framework has more flexibility and requires less substantial meteorological prior information than the synoptic classification methods. Comparison of these metrics in predicting excess daily mortality suggests that metrics thought to better characterize physiological heat stress by considering several weather conditions simultaneously may not be the same metrics that are better at predicting heat-related mortality, which has significant implications in HHWSs.

Geostatistical exploration of spatial variation of summertime temperatures in the Detroit metropolitan region.

BACKGROUND: Because of the warming climate urban temperature patterns have been receiving increased attention. Temperature within urban areas can vary depending on land cover, meteorological and other factors. High resolution satellite data can be used to understand this intra-urban variability, although they have been primarily studied to characterize urban heat islands at a larger spatial scale. OBJECTIVE: This study examined whether satellite-derived impervious surface and meteorological conditions from multiple sites can improve characterization of spatial variability of temperature within an urban area. METHODS: Temperature was measured at 17 outdoor sites throughout the Detroit metropolitan area during the summer of 2008. Kriging and linear regression were applied to daily temperatures and secondary information, including impervious surface and distance-to-water. Performance of models in predicting measured temperatures was evaluated by cross-validation. Variograms derived from several scenarios were compared to determine whether high-resolution impervious surface information could capture fine-scale spatial structure of temperature in the study area. RESULTS: Temperatures measured at the sites were significantly different from each other, and all kriging techniques generally performed better than the two linear regression models. Impervious surface values and distance-to-water generally improved predictions slightly. Restricting models to days with lake breezes and with less cloud cover also somewhat improved the predictions. In addition, incorporating high-resolution impervious surface information into cokriging or universal kriging enhanced the ability to characterize fine-scale spatial structure of temperature. CONCLUSIONS: Meteorological and satellite-derived data can better characterize spatial variability in temperature across a metropolitan region. The data sources and methods we used can be applied in epidemiological studies and public health interventions to protect vulnerable populations from extreme heat events.