Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves
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.
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.
- 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, 2010Google Scholar
- Hothorn T, Hornik K, Strobl C, Zeileis A (2014) party: A laboratory for recursive partytioning. R package version 1.0–19Google Scholar
- Liaw A, Wiener M (2014) randomForest: Breiman and Cutler’s random forests for classification and regression. R package version 4.6–10Google Scholar
- Lunardon N, Menardi G, Torelli N (2014) ROSE: a package for binary imbalanced learning. R J 6(1):79–89Google Scholar
- Ridgeway G (2013) gbm: Generalized boosted regression models. R package version 2.1Google Scholar
- Ripley BD (2015) tree: Classification and regression trees. R package version 1.0–35Google Scholar
- 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):5–79Google Scholar
- 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):573–579Google Scholar