Abstract
Heatwaves are divided between moderate, more common heatwaves and rare “high-mortality” heatwaves that have extremely large health effects per day, which we define as heatwaves with a 20 % or higher increase in mortality risk. Better projections of the expected frequency of and exposure to these separate types of heatwaves could help communities optimize heat mitigation and response plans and gauge the potential benefits of limiting climate change. Whether a heatwave is high-mortality or moderate could depend on multiple heatwave characteristics, including intensity, length, and timing. We created heatwave classification models using a heatwave training dataset created using recent (1987–2005) health and weather data from 82 large US urban communities. We built twenty potential classification models and used Monte Carlo cross-validations to evaluate these models. We ultimately identified several models that can adequately classify high-mortality heatwaves. These models can be used to project future trends in high-mortality heatwaves under different scenarios of a changing future (e.g., climate change, population change). Further, these models are novel in the way they allow exploration of different scenarios of adaptation to heat, as they include, as predictive variables, heatwave characteristics that are measured relative to a community’s temperature distribution, allowing different adaptation scenarios to be explored by selecting alternative community temperature distributions. The three selected models have been placed on GitHub for use by other researchers, and we use them in a companion paper to project trends in high-mortality heatwaves under different climate, population, and adaptation scenarios.
Similar content being viewed by others
References
Anderson GB (2014) Commentary: Tolstoy’s heat waves: each catastrophic in its own way? Epidemiology 25(3):365–367
Anderson GB, Bell ML (2009) Weather-related mortality: how heat, cold, and heat waves affect mortality in the United States. Epidemiology 20(2):205–213
Anderson GB, Bell ML (2011) Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 US communities. Environ Health Perspect 119(2):210–218
Bell ML, Dominici F (2010) Challenges and research needs in climate change and human health: A case study on heat waves. NSF workshop on “Mathematical Challenges in Sustainability”, DIMACS, Rutgers, New Jersey, November 15–17, 2010
Curriero F, Heiner K, Samet J, Zeger S, Strug L, Patz J (2002) Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol 155(1):80–87
Everson PJ, Morris CN (2000) Inference for multivariate normal hierarchical models. J Roy Stat Soc B 62(2):399–412
Gosling SN, McGregor GR, Paldy A (2007) Climate change and heat-related mortality in six cities. Part I: model construction and validation. Int J Biometeorol 51(6):525–540
Gosling SN, McGregor GR, Lowe JA (2009) Climate change and heat-related mortality in six cities. Part 2: climate model evaluation and projected impacts from changes in the mean and variability of temperature with climate change. Int J Biometeorol 53:31–51
Hayhoe K, Cayan D, Field CB, et al. (2004) Emissions pathways, climate change, and impacts on California. Proc Natl Acad Sci U S A 101(34):12422–12427
Hothorn T, Hornik K, Strobl C, Zeileis A (2014) party: A laboratory for recursive partytioning. R package version 1.0–19
James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer, New York
Kalkstein LS, Greene JS (1997) An evaluation of climate / mortality relationship in large US cities and the possible impacts of climate change. EHP 105(1):84–93
Knowlton K, Lynn B, Goldberg RA, et al. (2007) Projecting heat-related mortality impacts under a changing climate in the New York City region. Am J Public Health 97(11):2028–2034
Knowlton K, Rotkin-Ellman M, King G, et al. (2009) The 2006 California heat wave: impacts on hospitalizations and emergency department visits. Environ Health Perspect 117(1):61–67
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28(5):1–26
Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, New York
Liaw A, Wiener M (2014) randomForest: Breiman and Cutler’s random forests for classification and regression. R package version 4.6–10
Luber G, McGeehin M (2008) Climate change and extreme heat events. Am J Prev Med 35(5):429–435
Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. R J 6(1):79–89
Meehl G, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305(5686):994–997
Meehl G, Goddard L, Boer G, et al. (2014) Decadal climate prediction: an update from the trenches. BAMS 95(2):243–267
Mills D, Schwartz J, Lee M, et al. (2014) Climate change impacts on extreme temperature mortality in select metropolitan areas in the United States. Clim Chang. doi:10.1007/s10584-014-1154-8
O’Neill MS, Ebi KL (2009) Temperature extremes and health: Impacts of climate variability and change in the United States. J Occup Environ Med 51(1):13–25
Oleson KW, Anderson GB, Jones B, McGinnis SA, Sanderson B (2015) Avoided climate impacts of urban and rural heat and cold waves over the U.S. using large climate model ensembles for RCP8.5 and RCP4.5. Clim Chang. doi:10.1007/s10584-015-1504-1
Peng RD, Bobb JF, Tebaldi C, McDaniel L, Bell ML, Dominici F (2011) Toward a quantitative estimate of future heat wave mortality under global climate change. Environ Health Perspect 119(5):701–706
R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna http://www.R-project.org/
Ridgeway G (2013) gbm: Generalized boosted regression models. R package version 2.1
Ripley BD (2015) tree: Classification and regression trees. R package version 1.0–35
Rocklov J, Ebi KL (2012) High dose extrapolation in climate change projections of heat-related mortality. J Agric Biol Environ Stat 17(3):461–475
Samet JM, Zeger SL, Dominici F, et al. (2000) The national morbidity, mortality, and air pollution study. Part II: morbidity and mortality from air pollution in the United States. Res Rep Health Eff Inst 94(Pt.2):5–79
Wainwright SH, Buchanan SD, Mainzer M, Parrish RG, Sinks TH (1999) Cardiovascular mortality—the hidden peril of heat waves. Prehosp Disaster Med 14(4):222–231
White-Newsome JL, Ekwurzel B, Baer-Schultz M, Ebi KL, O’Neill MS, Anderson GB (2014) Survey of county-level heat preparedness and response to the 2011 summer heat in 30 US states. EHP 122(6):573–579
Whitman S, Good G, Donoghue ER, Benbow N, Shou W, Mou S (1997) Mortality in Chicago attributed to the July 1995 heat wave. Am J Public Health 87(9):1515–1518
Acknowledgments
G.B. Anderson and R.D. Peng were supported by NIEHS grants R00ES022631 and R21ES020152 and by NSF grant 1331399. Material contributed by K.W. Oleson is based upon work supported by the National Science Foundation, Grant Number AGS-1243095, in part by NASA grant NNX10AK79G (the SIMMER project), and by the NCAR Weather and Climate Impacts Assessment Science Program. Brian O’Neill, Claudia Tebaldi, and Andrew Gettelman provided helpful suggestions.
Author information
Authors and Affiliations
Corresponding author
Additional information
This article is part of a Special Issue on “Benefits of Reduced Anthropogenic Climate ChangE (BRACE)” edited by Brian O’Neill and Andrew Gettelman.
Electronic supplementary material
ESM 1
(PDF 165 kb)
Rights and permissions
About this article
Cite this article
Anderson, G.B., Oleson, K.W., Jones, B. et al. Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves. Climatic Change 146, 439–453 (2018). https://doi.org/10.1007/s10584-016-1776-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10584-016-1776-0