Extreme temperature mortality under present climate
Figure 1 shows Medina-Ramon and Schwartz (2007) found an extremely hot day generally increases daily mortality by roughly three times as much as an extremely cold day (~6 and ~2 %, respectively).
Online Resource 2 presents the projected number of extremely cold and hot days for 1999–2001 and the corresponding average number of each type of day for the studied cities. Online Resource 3 presents the calculated number of deaths from extremely cold and extremely hot days in each city for the 2000 reporting year using the average number of each type of extreme temperature day (see Section 2.3 for the calculation description). Note that the results of this analysis do not reflect the directly observed meteorological data in each city, but rather the average of IGSM-CAM’s projected data for the years 1999–2001. These values provide the baseline we use to calculate future changes in mortality from each type of extreme weather day under different emissions scenarios.
Extreme temperature mortality under future climates
Table 1 presents a summary of the number of extremely hot and cold days in the three time periods for the two emissions scenarios (REF and POL3.7) across the studied cities.
The REF scenario results in Table 1 reflect anticipated warming over the 21st century in the absence of GHG emissions controls. In contrast, under the POL3.7 scenario, the increase in extremely hot days by 2050 is approximately 75 % less than the REF case as a result of the emissions controls. Further, there is little increase in the number of extremely hot days from 2050 to 2100 under the POL3.7 scenario. In contrast, the average number of extremely hot days more than triples over this 50-year period in the REF scenario. For extremely cold days, the warming under both the Policy 3.7 and REF scenarios dramatically reduces the number of cities with identifiable extremely cold days by 2050 (nine cities for the POL3.7 scenario and five for the REF scenario). By 2100, in the REF scenario, only one city has a projected extremely cold day, while in the POL3.7 scenario, eight cities are still projected with extremely cold days. This is consistent with the observed warming stabilization described above for the extremely hot days.
Online Resources 4 and 5 provide detail on the projected number of extremely hot days by city for each emission scenario and each of the 3 years in the 2050 and 2100 reporting period and the associated 3-year average. In particular, these results highlight IGSM-CAM’s interannual variability.
Table 2 presents the mortality results across the cities for the projected changes in extreme temperature days. The top of Table 2 presents projected changes in mortality from extreme temperature days compared to the year 2000 baseline across all cities for the REF and POL3.7 emissions scenarios holding the population at 2010 levels. The bottom of Table 2 provides these results after applying the ICLUS-based, city-specific population adjustment factors described in Section 2.4.
Table 2 highlights the following results:
Increases in projected deaths from extremely hot days
Decreases in projected deaths from extremely cold days
Increases in combined projected deaths from extremely hot and cold days within a year and scenario.
The last of these results, with the population held constant at 2010 levels (top half of Table 2), explicitly addresses the question of the net mortality impact of climate change on future extreme temperature days. The reduction in projected deaths from extremely cold days is more than offset by the projected increase in deaths from extremely hot days. This result holds for all reported future years for the POL3.7 and REF scenarios As a result, climate change clearly reflects an increasing health risk from extreme temperatures. Incorporating anticipated population changes (bottom half of Table 2) only reinforces this conclusion, as increasingly large populations would be exposed to the projected extreme temperatures.
Benefits of a climate mitigation policy
Table 3 presents estimates of the benefits of implementing GHG emissions controls. These results are calculated subtracting the POL3.7 results from the REF results from Table 2 in the different combinations of extreme temperatures and reporting years.
Table 3 consistently shows implementing emissions controls would reduce the future risk of deaths from extremely hot days (i.e., mortality values greater than zero). The indication of a disbenefit with respect to future mortality from extremely cold days reflects the reduced warming associated with the POL3.7 scenario. However, this small disbenefit is overwhelmed by the reduced mortality from extremely hot future days. The net benefit associated with the POL3.7 implementation increases over time and when projected population changes are accounted for.
Figure 2 provides an alternative summary of the benefits of implementing the POL3.7 scenario by representing how the mortality rate from projected extremely hot and cold days combined changes over time. In this figure, the increased mortality associated with the REF scenario is clearly seen with the increase in size in the representative mortality circles over time. In contrast, the combined mortality rates with the POL3.7 scenario in future years do not show this dramatic increase. Figure 2 shows that most of the dramatic increases in mortality rates from the baseline to the 2050 period within the REF scenario occur in the Central U.S. By 2100 however, in the REF scenario, the southern and East Coast cities will also experience large increases in mortality rates relative to the year 2000 baseline.
The results in the first two columns of Tables 2 and 3 explicitly exclude consideration of the possibility of there being an adaptive response over time to extreme temperatures. To reflect potential adaptation over time we also evaluated the impacts of the changing climate over time by adjusting the threshold temperatures in the emissions scenarios as follows:
Extreme heat days: the threshold temperature value in each city was increased to the highest daily minimum temperature value in the original group of 33 cities (27.2 °C in Dallas–equivalent to 81.0 °F)
Extreme cold days: the threshold temperature value in each city was increased to the highest daily maximum temperature value in the original group of 33 cities (7.2 °C from New Orleans–equivalent to 45.0 °F)
These adjustments, which do not include any change in the original city-specific mortality impact factors, reflect a hybrid of prior adaptation approaches of assuming a uniform, but equivalent increase in threshold temperatures (e.g., Gosling et al. 2009) and the use of an analogue city approach (see Kalkstein and Greene 1997; Knowlton et al. 2007). This adjustment is also consistent with research that has argued that even with climate change there will still be wintertime mortality from extremely relatively cold days (e.g., Kinney et al. 2012), This is consistent with a view that it is the relative shock of these days that is most important, not the absolute temperature. Further, this assumption was applied only to the projected climate data for the 3-year period centered around 2100 as this is when the impact of these adjustments is greatest.
The results of this analysis, presented in the third column of Tables 2 and 3, follow initial expectations. Specifically, with the warmer threshold temperatures the number of deaths from extremely cold days increases as more days in each location satisfy the new criterion. At the same time, the number of deaths attributable to extremely hot days declines as fewer days in the cities satisfy the revised criterion. However, the net result is that, while smaller than the original results, implementing POL3.7 still provides a net benefit considering the combined effects for both types of days. This result further supports the conclusion that warming as a result of projected climate changes will lead to a net increase in mortality risks/mortality.