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BackgroundA warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships.MethodsWe used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100.ResultsWe found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city.ConclusionsWe found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-015-0071-2) contains supplementary material, which is available to authorized users.

This paper develops and applies methods to quantify and monetize projected impacts on terrestrial ecosystem carbon storage and areas burned by wildfires in the contiguous United States under scenarios with and without global greenhouse gas mitigation. The MC1 dynamic global vegetation model is used to develop physical impact projections using three climate models that project a range of future conditions. We also investigate the sensitivity of future climates to different initial conditions of the climate model. Our analysis reveals that mitigation, where global radiative forcing is stabilized at 3.7 W/m 2 in 2100, would consistently reduce areas burned from 2001 to 2100 by tens of millions of hectares. Monetized, these impacts are equivalent to potentially avoiding billions of dollars (discounted) in wildfire response costs. Impacts to terrestrial ecosystem carbon storage are less uniform, but changes are on the order of billions of tons over this time period. The equivalent social value of these Climatic Change (2015) The magnitude of these results highlights their importance when evaluating climate policy options. However, our results also show national outcomes are driven by a few regions and results are not uniform across regions, time periods, or models. Differences in the results based on the modeling approach and across initializing conditions also raise important questions about how variability in projected climates is accounted for, especially when considering impacts where extreme or threshold conditions are important.

Background:The public health community readily recognizes flooding and wildfires as climate-related health hazards, but few studies quantify changes in risk of exposure, particularly for vulnerable children and older adults.Objectives:This study quantifies future populations potentially exposed to inland flooding and wildfire smoke under two climate scenarios, highlighting the populations in particularly vulnerable age groups (≤4y old and ≥65y old).Methods:Spatially explicit projections of inland flooding and wildfire under two representative concentration pathways (RCP8.5 and RCP4.5) are integrated with static (2010) and dynamic (2050 and 2090) age-stratified projections of future contiguous U.S. populations at the county level.Results:In both 2050 and 2090, an additional one-third of the population will live in areas affected by larger and more frequent inland flooding under RCP8.5 than under RCP4.5. Approximately 15 million children and 25 million older adults could avoid this increased risk of flood exposure each year by 2090 under a moderate mitigation scenario (RCP4.5 compared with RCP8.5). We also find reduced exposure to wildfire smoke under the moderate mitigation scenario. Nearly 1 million young children and 1.7 million older adults would avoid exposure to wildfire smoke each year under RCP4.5 than under RCP8.5 by the end of the century.Conclusions:By integrating climate-driven hazard and population projections, newly created county-level exposure maps identify locations of potential significant future public health risk. These potential exposure results can help inform actions to prevent and prepare for associated future adverse health outcomes, particularly for vulnerable children and older adults. https://doi.org/10.1289/EHP2594

Multiple studies have identified links between climate and West Nile virus disease since the virus arrived in North America. Here we sought to extend these results by developing a Health Impact Function (HIF) to generate county-level estimates of the expected annual number of West Nile neuroinvasive disease (WNND) cases based on the county's historical WNND incidence, annual average temperature, and population size. To better understand the potential impact of projected temperature change on WNND risk, we used the HIF to project the change in expected annual number of WNND cases attributable to changing temperatures by 2050 and by 2090 using data from five global climate models under two representative concentration pathways (RCP4.5 and RCP8.5). To estimate the costs of anticipated changes, as well as to enable comparisons with other public health impacts, projected WNND cases were allocated to nonfatal and fatal outcomes, then monetized using a cost-of-illness estimate and the U.S. Environmental Protection Agency's value of a statistical life, respectively. We found that projected future temperature and population changes could increase the expected annual number of WNND cases to ≈2000 -2200 cases by 2050 and to ≈2700 -4300 cases by 2090, from a baseline of 970 cases. Holding population constant at future levels while varying temperature from a 1995 baseline, we estimated projected temperature change alone is responsible for ≈590 and ≈960 incremental WNND cases in 2050 and 2090 (respectively) under the RCP4.5 scenario, and ≈820 and ≈2500 cases in 2050 and 2090 (respectively) for the RCP8.5 scenario, with substantial regional variation. The monetized impact of these temperature-attributable incremental cases is estimated at $0.5 billion in 2050 and $1.0 billion in 2090 How to cite this