Impact of climate change on mortality in southeastern United States

There is a growing interest in quantifying the health impacts of climate change. Those studies routinely use climate model output as future exposure projections. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arising from different emission scenarios or multi-model ensembles. The objective comparison of mean and variances of modeled climatic variables with the ones obtained from observed field data is the common approach for assessment of computer model performance. One drawback of this strategy is that it fails to calibrate properly the tails of the modeled temperature distribution, and improving the ability of these numerical models to characterize extremely high temperature events is of critical interest to understand the potential impact of climate change on human health. In this work we introduce an innovative framework to assess climate model performance, not only based on the two first moments of models and field data, but on their entire distribution. Our methodology also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. This approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We apply the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculate the number of excess deaths attributed to heat waves and future temperatures in the southern region of the U.S.