Population ageing determines changes in heat vulnerability to future warming

We used the monthly mean temperature of high-resolution gridded datasets from the Climate Research Unit Time Series version 4.03 (CRUTS4) for 1901–2018 as an observation (Harris et al 2014). A summer is defined as the hottest season according to records of three consecutive months derived from the 20-year monthly climatology for 1986–2005 at individual grid cells (figure S1 (available online at stacks.iop.org/ERL/15/114043/mmedia)). Further, the summer with the highest temperature during 1901–2018 is defined as the hottest summer. Figure S2 shows the global distribution of the seasonal mean temperature and year of occurrence of the hottest summer over the land surface.

Seasonal mean temperature during summer is projected using the monthly mean temperature for 1861–2005 and the RCP8.5 scenario for 2006–2100 obtained from 27 GCMs that participated in the fifth phase of the Climate Model Intercomparison Project (CMIP5) (Taylor et al 2011; table S1). The first ensemble member of the individual GCMs is interpolated to the half-degree in latitude and longitude and is adjusted to the observation using the bias-correction procedures and observation data from the Climate Prediction Center (see Feng and Fu 2013 for details). The occurrence of UHSs is determined every year for the period of 2006–2100 when the summer mean temperature exceeds the hottest summer temperature based on CRUTS4. The probability of UHSs occurring within 20-year moving windows is computed as the ratio of the number of UHS occurrences within each 20-year period to 20. Each 20-year period is indexed as its final year; thus, the 20-year moving windows for 2006–2100 are from 1987–2006 to 2081–2100. The probability of UHS occurrence is projected at the times when the global mean temperature crosses 1.5, 2, and 3 °C relative to the pre-industrial level (t_1.5, t₂, and t₃). Here, t_1.5, t₂, and t₃ are calculated as the times whereby the 20-year moving averaged global temperature initially reach 0.9, 1.4, and 2.4 °C above 1986–2005, respectively, because this period was probably, at least, 0.6 °C warmer than the pre-industrial level (Schleussner et al 2016, Hawkins et al 2017). The mean, 84th and 16th percentile values of the UHS probability of occurrence are calculated based on the spread of 27 GCM projections. Also, the 16%–84% range of the probability of UHS occurrence is computed as the difference between the 84th and 16th percentile values. Note that extreme heat waves, which can induce numerous damages on public health, generally occurred during the recorded hottest summer (Dole et al 2011, Steffen et al 2017, Yiou et al 2020), therefore, the use of UHSs to project exposure of older people in future can be appropriate.

We estimated the total population exposed to UHSs (POP_UHS) in six regions, China, India, sub-Sahara countries with low income (SSA-L), Brazil, the US, and the 15 countries that joined European Union prior to 2004 (EU15), at the year of 2040, 2050, and 2070, which are the nearest years to the ensemble median of t_1.5, t₂, and t₃, across 27 GCM simulations (2039, 2052, and 2075), respectively. The gridded dataset of the total population under SSP2, SSP3, and SSP5 for 2010–2100 with a 10-year temporal resolution is considered and interpolated to the half-degree in latitude and longitude (Jones and O'Neil 2016, Fricko et al 2017, Fujimori et al 2017, Gao 2017, Kriegler et al 2017). Both SSP3 and SSP5 can be relevant to the RCP8.5 scenario because these two scenarios are characterised by large emissions from fossil-fuel usage (Riahi 2017). The SSP2 scenario is selected as a middle-of-the-road condition (Riahi 2017). Thus, three SSPs can provide a suitable range of population situations relevant to climate change under the RCP8.5 scenarios. Among the three SSP scenarios, SSP3 and SSP5 show the largest and smallest increase in population with slow and rapid ageing, respectively, and SSP2 represents the middle-of-the-road between SSP3 and SSP5 (table S2; Kc and Lutz 2017). Six regions are selected as those with high populations in individual continents, i.e. East and South Asia, Africa, South and North America, and Europe. The division of regions follows the 32-region level of the SSP database (https://tntcat.iiasa.acat/SspDb). For individual regions, the values of POP_UHS are estimated as the summation of the population in each grid cell where UHSs occur within the regarding regions (figure S3). Note that population of those six regions represented 61.4% of the world's population in 2010 (Kc and Lutz 2017).

The projected regional-level population structure dataset under SSP2, SSP3, and SSP5 for 2005–2100 with 5-year discrete temporal resolutions obtained from the SSP database are used to estimate the P65+_UHS for 2040, 2050, and 2070 (Kc and Lutz 2017). Initially, the ratio of the P65+ to the total population count (R_P65+) is calculated in individual regions. Under an assumption that R_P65+ is the same for all grid cells within individual regions, P65+_UHS is computed as the product of POP_UHS and R_P65+. Note that this assumption is used to match the gridded population count and regional-level composition of population data.

We calculated the change in P65+_UHS in 2070 relative to that in 2040, wherein the global mean temperature exceeded approximately 1.5 °C above the pre-industrial level under the RCP8.5 scenario. The change in the P65+_UHS relies on changes in three factors, namely, the UHS occurrence, total population count, and the ratio of P65+ to total population. The impact of individual factors is designated by impacts of warming (WARM), population change (POP), and ageing (AGEING). Here, we estimated the impact of each factor on the change in the P65+_UHS by subtracting the change in the P65+_UHS, considering two factors different from all the factors. For example, the impact of POP on the change in the P65+_UHS is computed as the difference between the P65+_UHS change due to the combined impacts and that due to WARM and AGEING. The estimated impacts of individual factors on P65+_UHS can explain which factor causes the change in P65+_UHS, as well as the associated potential risks.

The multi-model ensemble of probability of UHS occurrence derived from the 27 GCM projections increases with the magnitude of global warming as the summer mean temperature rises (figure 1(a)–(c) and S4). In the 1.5 °C warming scenario, the probability of UHS occurrence shows a distinct regional variation (figure 1(a)). UHSs are likely to occur in many tropical regions, indicating that the people in these regions are at risk of being exposed to UHSs and the associated heat stresses. In contrast, the probability of UHS occurrence is much lower in mid- and high latitudes, except for some regions in North America. This spatial UHS probability of occurrence pattern is connected to the low and high internal variabilities of temperature in the tropics and high latitudes (Harrington et al 2016, Lehner et al 2018). The probability increases under the 2 °C global warming condition relative to that under the 1.5 °C scenario (figure 1(b)). In particular, the likelihood of UHS occurrence in North America, East Asia, and Southeast Asia shows an increase of more than 30%, whereas the probability is low in Europe, Siberia, and southeastern Australia. When the global mean temperature reaches 3 °C, the summer mean temperature is likely to exceed the observed record of the hottest summer over almost all of the land surface (figure 1(c)). The regions with high UHS probabilities of occurrence are generally consistent with the areas where the 16%–84% range of UHS probability of occurrence is small, thereby suggesting that the possibility of UHSs occurring is high in those regions (figure 1).

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Projecting POP_UHS is important for estimating the potential impacts of UHSs on society. In general, more people will experience UHSs in the future due to world population growth, as well as the increase of the probability of UHSs in many areas (Lehner et al 2018, Harrington and Friederike 2018). However, the POP_UHS can vary by regions and population scenarios. Figure 2 shows the estimated ensemble median and the 16%–84% range of POP_UHS for the population scenario of SSP2, SSP3, and SSP5 across 27 future projections in China, India, SSA-L, Brazil, US, and EU15 at 2040, 2050, and 2070. In China, the POP_UHS slightly increases in 2050 relative to that in 2040, and then decreases in 2070 under all population scenarios despite a notable increase in the probability of UHS occurrence because of the decrease in total population since 2030 (figures 1(a)–(c) and 2(a)). In the other five regions, the POP_UHS increases under all population scenarios, particularly in SSA-L, according to both the increase in the probability of UHS occurrence and total population (figures 1(a)–(c) and 2(b)–(f)). The change in POP_UHS largely varies according to the population scenarios except for China. In India, SSA-L, and Brazil, the increase in the ensemble median of POP_UHS under SSP3 reaches 876, 1398, 101 million, which represent a triple, double, and double increase to that under SSP5, respectively (table S3). In contrast, the increment of POP_UHS is the largest and smallest under SSP5 and SSP3 in the US and EU15, respectively (figures 2(e) and (f)). The change in POP_UHS under the SSP2 scenario lies between that in SSP3 and SSP5 in all regions.

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The population structure evolves differently according to regions and population scenarios (Kc and Lutz 2017); thus, temporal changes in P65+_UHS are different to those of POP_UHS( figures 2 and 3). For all regions, the increasing rate of P65+_UHS is larger than that of POP_UHS under all SSP scenarios. China shows a large increase in P65+_UHS, which contrasts the slight change in POP_UHS( figures 2(a) and 3(a)). In India, under SSP2, the ensemble median of P65+_UHS in 2070 is three times larger than that in 2040, while POP_UHS increases by 1.5 times for the same periods (figures 2(b) and 3(b); tables S3 and S4). The P65+_UHS in the other four regions are also projected to increase drastically (figure 3(c)–(f)). In addition, the changes in P65+_UHS according to individual SSPs are different from those in the POP_UHS values in China, India, SSA-L, and Brazil. In these regions, the increase in P65+_UHS is the largest and smallest under the SSP5 and SSP3 scenarios, respectively, which is contrary to those in POP_UHS( figures 2(a)–(d) and 3(a)–(d)). For example, in India, the increase in the ensemble median of POP_UHS in 2070 relative to that in 2040 is 876 million under SSP3, which is three times larger than that under SSP5 (tables S3 and S4). However, for the same period, the increase in the ensemble median of P65+_UHS in India reaches 173 million under SSP3, which is less than two-thirds of that under SSP5 (tables S3 and S4). Both the US and EU15 show consistent changes in POP_UHS and P65+_UHS for the three SSPs (figures 2(e), (f), (e), and 3(f)). The contrasting responses of POP_UHS and P65+_UHS to the population scenarios in China, India, SSA-L, and Brazil are consequent of the distinct evolution of the elderly population to that of the total population (inner boxes in figures 2 and 3), thereby indicating that ageing should be considered as an important factor for estimating potential risks from heat stresses.

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The change in P65+_UHS depends on three factors: (1) increase in UHS occurrence according to warming (WARM), (2) change in total population (POP), and (3) change in the ratio of P65+ to total population (AGEING). The impacts of WARM, POP, and AGEING on the change in P65+_UHS in 2070 relative to that in 2040 are separated to examine the contribution of individual factors, which vary according to regions and population scenarios. Figure 4 shows the projected difference in P65+_UHS between 2070 and 2040 according to the combined impacts and individual factors for all regions and population scenarios. In China, the large increase in P65+_UHS results from the WARM and AGEING under all SSP scenarios (figure 4(a)). AGEING is the largest contributor to the increase in P65+_UHS in India, SSA-L, and Brazil under population scenarios following SSP2 and SSP5, which represent moderate- and fast-ageing scenarios, respectively (figure 4(b)–(d)). When population change follows the slow ageing scenario SSP3, the combined impacts of the three factors increase P65+_UHS in China, India, SSA-L, and Brazil (figure 4(a)–(d)). In the US and EU15, the WARM is the primary reason for the increase in P65+_UHS rather than the other considered parameters under all population scenarios (figures 4(e) and (f)). This can be explained by the remarkable increase in the probability of UHS occurrence around 2070, as well as the relatively small change in the total population and P65+ in the US and EU15 relative to that in 2040 (figures 1, 2(e), (f), 3(e), and (f)).

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Our results predict that more elderly people will be exposed to heat stresses occasioned by UHSs due to the combined impacts of WARM, POP, and AGEING. Furthermore, the individual contributions of the aforementioned phenomena towards increasing P65+_UHS vary by region. In China, India, SSA-L, and Brazil, the ageing-driven increase in P65+_UHS in 2070 relative to that in 2040 is larger than the impact of warming and population change, except in the SSP3 scenario (figure 4(a)–(d)). This indicates that the potential impacts of UHSs on public health will increase due to ageing in those regions even if we limit global warming to the level in 2040, comparable to 1.5 °C above the pre-industrial level. In addition, the health risks resulting from warming and ageing may be amplified in the aforementioned regions due to the relatively deficient economic capabilities (Harrington et al 2016). Thus, the economic support of developed countries is necessary for developing regions to establish age-friendly health and social services to mitigate the threat of UHSs to the aged societies of the future. In contrast, in both the US and EU15, the growth in P65+_UHS according to warming and the associated increase in the probability of UHS occurrence is much larger than that due to the change in other factors under all population scenarios (figures 4(e) and (f)). In addition, the regulated strength of UHSs under a target warming level, such as a 1.5 °C warming relative to the pre-industrial period, can suppress heat stresses in other regions that experience rapid ageing (King et al 2018, Lehner et al 2018). Efforts on traditional mitigation strategies that focus on reducing the emission of greenhouse gases and the relevant global warming effects are essential to reduce health problems associated with unprecedented summer heat in an aged society.