Rapid increase in the risk of heat-related mortality

Abstract

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives.

Fig. 1. Risk of heat mortality for São Paulo, Paris and Bangkok.

Risk of heat mortality for São Paulo (Brasil, a, d, g), Paris (France, b, e, h) and Bangkok (Thailand, c, f, i). a–c Relative risk of mortality relative to the location-specific minimum mortality temperatures reported as best linear unbiased predictions (BLUPs) with 95% confidence interval (shaded area). Vertical dotted lines show the log-linear extrapolation used for projections when future temperatures exceed current temperatures. Dashed vertical lines show present-day 99th percentile temperatures. d–f Impact exceedance frequency curves of annual heat mortality fractions for the observed years (black line, markers denote individual years), as well as the climate of 2000 (warming level of 0.7 °C), the climate of 2020 (warming level of 1.2 °C), 1.5 °C warming and 2 °C warming. The modelled impact exceedance frequency curves are reported as the median value over the five single-model initial-condition large ensembles (SMILEs). g–i Modelled magnitude of the annual heat mortality fraction from a 1-in-100 year season for different warming levels. Results are displayed for each SMILE (bars) including uncertainty estimates depicted with the 95% empirical confidence intervals accounting for the internal climate variability (inner whisker) and imprecision of the exposure-response associations (outer whisker). The black horizontal line denotes the median estimate for each global warming level.

Fig. 2. Changes in return periods of a 1-in-100-year season in the 748 locations.

Changes in return periods for the climate of 2020 (warming level of 1.2 °C, a), at 1.5 °C warming (b) and at 2.0 °C warming (c) compared to the risk in the climate of 2000 (0.7 °C warming). The figure displays the new return period of the location-specific 1-in-100-year heat-mortality level of 2000. The colour-scale is logarithmic. The grey dots denote locations with inconclusive results due to their spread in uncertainty.

Fig. 3. Heat-mortality fraction of a 1-in-100-year season in the 748 locations.

Heat-mortality fraction of a 1-in-100 year season. Rates are displayed for the climate of 2000 (a), 2020 (b), 1.5  °C (c) and 2.0 °C warming (d). For each location, shares are calculated as the heat-mortality counts during a 1-in-100 year season divided by the mean annual mortality. The colour-scale is logarithmic.

Fig. 4. Schematic display of internal variability of heat related mortality for Paris (France).

Each circle contains 100 points representing the climatic variability of the given warming level. The points denote the return period based magnitude of mortality of the climate of the year 2000 for a 1-in-10 year season (orange), a 1-in-100 year season (violet), a 1-in-500 year season (black) and more frequent seasons (yellow).