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      Toward a Quantitative Estimate of Future Heat Wave Mortality under Global Climate Change

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          Abstract

          Background

          Climate change is anticipated to affect human health by changing the distribution of known risk factors. Heat waves have had debilitating effects on human mortality, and global climate models predict an increase in the frequency and severity of heat waves. The extent to which climate change will harm human health through changes in the distribution of heat waves and the sources of uncertainty in estimating these effects have not been studied extensively.

          Objectives

          We estimated the future excess mortality attributable to heat waves under global climate change for a major U.S. city.

          Methods

          We used a database comprising daily data from 1987 through 2005 on mortality from all nonaccidental causes, ambient levels of particulate matter and ozone, temperature, and dew point temperature for the city of Chicago, Illinois. We estimated the associations between heat waves and mortality in Chicago using Poisson regression models.

          Results

          Under three different climate change scenarios for 2081–2100 and in the absence of adaptation, the city of Chicago could experience between 166 and 2,217 excess deaths per year attributable to heat waves, based on estimates from seven global climate models. We noted considerable variability in the projections of annual heat wave mortality; the largest source of variation was the choice of climate model.

          Conclusions

          The impact of future heat waves on human health will likely be profound, and significant gains can be expected by lowering future carbon dioxide emissions.

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          Most cited references52

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          Impact of regional climate change on human health.

          The World Health Organisation estimates that the warming and precipitation trends due to anthropogenic climate change of the past 30 years already claim over 150,000 lives annually. Many prevalent human diseases are linked to climate fluctuations, from cardiovascular mortality and respiratory illnesses due to heatwaves, to altered transmission of infectious diseases and malnutrition from crop failures. Uncertainty remains in attributing the expansion or resurgence of diseases to climate change, owing to lack of long-term, high-quality data sets as well as the large influence of socio-economic factors and changes in immunity and drug resistance. Here we review the growing evidence that climate-health relationships pose increasing health risks under future projections of climate change and that the warming trend over recent decades has already contributed to increased morbidity and mortality in many regions of the world. Potentially vulnerable regions include the temperate latitudes, which are projected to warm disproportionately, the regions around the Pacific and Indian oceans that are currently subjected to large rainfall variability due to the El Niño/Southern Oscillation sub-Saharan Africa and sprawling cities where the urban heat island effect could intensify extreme climatic events.
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            The use of the multi-model ensemble in probabilistic climate projections.

            Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of verification of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.
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              Asymptotic Statistics

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                Author and article information

                Journal
                Environ Health Perspect
                Environmental Health Perspectives
                National Institute of Environmental Health Sciences
                0091-6765
                1552-9924
                May 2011
                30 December 2010
                : 119
                : 5
                : 701-706
                Affiliations
                [1 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
                [2 ]Climate Central, One Palmer Square, Princeton, New Jersey, USA
                [3 ]Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
                [4 ]National Center for Atmospheric Research, Boulder, Colorado, USA
                [5 ]School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, USA
                [6 ]Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
                Author notes
                Address correspondence to R.D. Peng, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe St., E3527, Baltimore, MD 21205 USA. Telephone: (410) 955-2468. Fax: (410) 955-0958. E-mail: rpeng@ 123456jhsph.edu

                C. Tebaldi is employed by Climate Central. The authors declare they have no actual or potential competing financial interests.

                Article
                ehp-119-701
                10.1289/ehp.1002430
                3094424
                21193384
                f149c877-0f64-4953-a16f-97d99ed9c1f8
                This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original DOI.
                History
                : 12 May 2010
                : 29 December 2010
                Categories
                Research

                Public health
                extreme weather events,population health,global warming,climate models,time-series models

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