Heat Stress Response to National-Committed Emission Reductions under the Paris Agreement
Abstract
:1. Introduction
2. Data and Methods
2.1. Emission Scenarios
2.2. Estimation of Heat Stress Response to Emissions
2.2.1. Heat Stress Index Calculation
2.2.2. Estimated Heat-Stress Change under the Paris Agreement
- Firstly, to assess the global mean warming level induced by each greenhouse gas emission scenario. Based on the 78 climate sensitivity experiments from the earth system models (ESMs) ensemble of CMIP5 [26], we assessed the possible corresponding global mean temperature rise for various emission scenarios [22,23]; we also integrated several other studies (temperature rise levels for some pathways have been provided) [27,28,29]. After a comprehensive assessment, we determined the most likely range of temperature increase for the continuous and delayed mitigation pathways of INDCs (Figure 1b).
- Secondly, to estimate the space pattern of heat-stress change. We used 14 ESMs from the CMIP5 archives. These models have different levels of climate sensitivity [30] and represent a wide range of climate responses to emission scenarios. Supplementary Table S2 summarizes information regarding the 14 ESMs used in this study. All model data were interpolated to a common 1.5° × 1.5° horizontal grid. The spatial pattern of the heat stress in response to each scenario was identified using a time-slice approach, where the spatial state at a specific warming point related to (and the global targets of the 1.5 °C and 2 °C scenarios) was separately obtained from the decadal time slices with the respective mean warming for each model. The spatial simulations of future heat-stress change were based on the CMIP5 models ensemble and the 5–95th percentile confidence intervals (CIs) were estimated from 10,000 bootstrapped subsamples of data. The period of 1980–2010 was referred to as the present-day baseline and the preindustrial period was defined as 1861–1900.
- Lastly, to evaluate population exposure to extreme heat stress. We used the population data from socioeconomic development scenarios of SSPs [31] and the data were changed to a 1.5 ° × 1.5° latitude/longitude grid to match the GCM resolution. The population exposed to an extreme wet-bulb temperature was calculated at a daily time resolution. If the wet-bulb temperature at a given grid cell exceeded the threshold value on a given day (for example, a wet-bulb temperature of 32 °C or 30 °C), then the grid cell was considered to be exposed, and the total number of exposed cells was counted in units of person-days. The total amount of annual exposure (in units of person-days) is the number of people multiplied by time. The uncertainty of the population exposure value is calculated by taking the 5–95th percentile confidence intervals of 14 GCMs (to reduce the influence of predicted temperature outliers in several GCMs).
3. Results
3.1. Global Heat-Stress Extreme Distribution
3.2. Global Heat-Stress Exposure Frequency
3.3. Global Population Exposure to Critical Heat Events
4. Discussion
5. Conclusions
- With the combination of temperature and humidity under climate change, heat stress intensity in the middle- and low-latitude regions will increase significantly. Under the delayed mitigation scenario, by the end of the 21st century, eastern China, South Asia, the Amazon, western Africa, the eastern United States, northern Australia, and other regions will be particularly vulnerable to heat stress; among them, South Asia, eastern China, and the Amazon are the most vulnerable. While under the INDC continuous mitigation efforts, the wet-bulb temperature of these three regions would decrease 1–2 °C in 2070–2100. Under the 1.5 °C and 2 °C global target scenarios, the predicted wet-bulb temperature of most regions, according to the majority of the models is below 29 °C.
- The frequency of exposure to the dangerous wet-bulb temperature threshold (which may severely affect human labor productivity) also potentially increases. In particular, the heat-stress exposure duration is the longest along the India-Pakistan border region of South Asia, and temperatures exceeding 30 °C and 32 °C during the period 2070–2100 are expected to occur more than 40 days and several to ten days per year, respectively (under the delayed mitigation scenario). Continuous mitigation efforts (compared to the delayed mitigation scenario) can significantly shorten the days and hours of heat exposure in India, but compared with the 1.5 °C and 2 °C global target scenarios, continuous mitigation efforts may still lead to higher exposure risks of heat stress.
- Populations exposed to dangerous wet-bulb temperatures are expected to continue to increase with climate change. More active mitigation policies could reduce population exposure (to wet-bulb temperatures greater than 30–32 °C) by approximately one order of magnitude (pursuing the INDC continuous mitigation scenario versus the delayed mitigation scenario), and half to one order of magnitude (the 2 °C global target scenario versus the INDC continuous mitigation scenario). Given the dramatic increase in the number of people worldwide who may be exposed to dangerous heat stress, failure to take proactive mitigation and adaptation measures in the future will likely result in greater economic losses and increased heat-related mortality.
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Wang, F.; Zhang, J. Heat Stress Response to National-Committed Emission Reductions under the Paris Agreement. Int. J. Environ. Res. Public Health 2019, 16, 2202. https://doi.org/10.3390/ijerph16122202
Wang F, Zhang J. Heat Stress Response to National-Committed Emission Reductions under the Paris Agreement. International Journal of Environmental Research and Public Health. 2019; 16(12):2202. https://doi.org/10.3390/ijerph16122202
Chicago/Turabian StyleWang, Fang, and Jintao Zhang. 2019. "Heat Stress Response to National-Committed Emission Reductions under the Paris Agreement" International Journal of Environmental Research and Public Health 16, no. 12: 2202. https://doi.org/10.3390/ijerph16122202