Performance of Excess Heat Factor Severity as a Global Heatwave Health Impact Index

Abstract

The establishment of an effective policy response to rising heatwave impacts is most effective when the history of heatwaves, their current impacts and future risks, are mapped by a common metric. In response meteorological agencies aim to develop seamless climate, forecast, and warning heat impact services, spanning all temporal and spatial scales. The ability to diagnose heatwave severity using the Excess Heat Factor (EHF) has allowed the Australian Bureau of Meteorology (the Bureau) to publicly release 7-day heatwave severity maps since 2014. National meteorological agencies in the UK and the United States are evaluating global 7-day and multi-week EHF heatwave severity probability forecasts, whilst the Bureau contributes to a Copernicus project to supply the health sector with global EHF severity heatwave projection scenarios. In an evaluation of impact skill within global forecast systems, EHF intensity and severity is reviewed as a predictor of human health impact, and extended using climate observations and human health data for sites around the globe. Heatwave intensity, determined by short and long-term temperature anomalies at each locality, is normalized to permit spatial analysis and inter-site comparison. Dimensionless heatwave event moments of peak severity and accumulated severity are shown to correlate with noteworthy events around the globe, offering new insights into current and future heatwave variability and vulnerability. The EHF severity metric permits the comparison of international heatwave events and their impacts, and is readily implemented within international heatwave early warning systems.

Keywords: early warning system; heatwave event moments; heatwave impact; heatwave index; heatwave intensity; heatwave severity.

Figure A1

Daily maximum and minimum temperatures for Adelaide heatwaves in 2006 (a) and 2009 (b). 25 °C reference temperature (yellow line).

Figure A2

Distribution function of daily temperature for all days in 1971 to 2000 climate reference period. Grey shade shows 95th percentile tail for all heatwaves present for this reference period.

Figure A3

Distribution function of EHI_accl for 1960 to 2018 climate period. Shaded region >1 shows acclimatization distribution samples when calculating EHF.

Figure A4

Acclimatization (blue) and significance (red) Excess Heat Indices for Adelaide’s 2006 (a) and 2009 (b) heatwaves, calculated using site data.

Figure A5

Excess Heat Factor for Adelaide’s 2006 (a) and 2009 (b) heatwaves, calculated using site data.

Figure A6

Distribution function of EHF for period 1887 to 2018. Values greater than zero shown in yellow are heatwaves. Maximum EHF value in distribution is indicated by dashed red line.

Figure A7

Adelaide empirical cumulative distribution of positive EHF (green line), overlain with the modelled generalized Pareto distribution (black dashes), and showing the 85th percentile (transition point) for determining the severe EHF threshold (dashed yellow lines). Extreme EHF threshold is shown in red (dashed red lines).

Figure A8

Excess Heat Factor for Adelaide’s 2006 (a) and 2009 (b) heatwaves, calculated using site data. Dimensionless heatwave severity [ ] and intensity (EHF, [°C²]) on left and right y-axes respectively.

Figure 1

Heat related mortality (black line, left axis) and EHF severity (blue line, right axis) for Adelaide 2009 extreme heatwave [35] (p. 21).

Figure 2

Heat related morbidity (ambulance movements, black line, left axis) and EHF severity (blue line, right axis) for Melbourne 2009 extreme heatwave [35] (p. 22).

Figure 3

London (Heathrow) EHF severity and intensity for 1976 heatwave, calculated using site data. Dimensionless heatwave severity [ ] and intensity (EHF, [°C²]) on left and right y-axes respectively.

Figure 4

As per Figure 3. London (Heathrow) severity (blue) and mortality (black) for 1983 (a), 1989 (b), 1990 (c) and 1995 (d) heatwaves. Daily excess deaths and severity (dimensionless) on left and right y-axes, respectively.

Figure 5

As per Figure 4. London (Heathrow) severity (blue) and mortality (black) for 1996 heatwave. Daily excess deaths and severity (dimensionless) on left and right y-axes respectively.

Figure 6

Chicago severity (blue), excess all cause deaths (black) (Whitman et al. [36] (p. 1517, Figure 2)), heat related mortality (purple) (Whitman et al. [36] (p. 1516, Figure 1)), heat deaths (gold) (Kaiser et al. [37] (p. S159, Figure 1)), and intensive care admissions (green) (Dematte et al. [38] (p. 174, Figure 1)) for 1995 heatwave. Daily deaths and admissions, and severity [ ] on left and right y-axes respectively.

Figure 7

Paris severity (blue) and France excess mortality (black) for 2003 heatwave. Daily excess deaths (Poumadère et al. [39] (p. 1486, Figure 1)) and severity [ ] on left and right y-axes respectively.

Figure 8

Paris (Orly) EHF severity and intensity for 2003 spring and summer, calculated using site data. Dimensionless heatwave severity [ ] and intensity (EHF, [°C²)] on left and right y-axes respectively.

Figure 9

Guangzhou severity (blue) and excess mortality (black) for 2005 heatwave. Daily excess deaths (Jun Yang et al. [40] (p. 650, Figure 1)) and severity [ ] on left and right y-axes respectively.