An ensemble projection of GDP and population exposure to high temperature events over Jing-Jin-Ji district based on high resolution combined dynamical and statistical downscaling datasets

doi:10.12006/j.issn.1673-1719.2019.111

Abstract

Abstract:

Future changes of GDP and population exposure to high temperature in the 21st century over Jing-Jin-Ji district were investigated based on the high resolution (6.25 km) combined statistical and dynamical downscaling datasets under RCP4.5 scenario, as well as the population and GDP data under SSP2. Results show that, the high resolution downscaling projection could well reproduce the hazard distributions of high temperature events in the Jing-Jin-Ji district. At the end of the 21st century, the significantly increased frequency of sultry events over the southeastern plain and coastal areas of Jing-Jin-Ji district will be found, so will hot events in the midland of Hebei. The areas with high population and GDP exposure are mainly located in cities such as Beijing, Tianjin, Baoding, Shijiazhuang and Handan etc., who are featured by developed economy, convenient transportation and large population, and their surrounding areas. In the 21st century, the increasing GDP exposure over Jing-Jin-Ji district can also be found. The exposure average by the end of the 21st century is about 58.9 times of the reference period. All the cities also present a consistent increase. In the mid-21st century, the regional average of population exposure in Jing-Jin-Ji district will reach its maximum value as 2.3 times that of the reference period. But the average population exposure in Beijing, Zhangjiakou, Chengde and Tangshan will grow continuously in the 21st century. The change of GDP exposure mainly subjects to non-linear factors, and the contribution rate increases with time reaching 70.9% at the end of the 21st century. While the change of population exposure in the near and medium term of the 21st century mainly subjects to non-linear factors. The contribution rate of climate factors increases with time, reaching a dominant position at the end of the 21st century.

Key words: Regional climate model, Climate change, High temperature, Exposure, Jing-Jin-Ji district

LI Rou-Ke, HAN Zhen-Yu, XU Ying, SHI Ying, WU Jia. An ensemble projection of GDP and population exposure to high temperature events over Jing-Jin-Ji district based on high resolution combined dynamical and statistical downscaling datasets[J]. Climate Change Research, 2020, 16(4): 491-504.

Figures/Tables 10

Fig. 1 Taylor diagram for ensemble average simulation error of TX, TN and RH in the summer half year by using the station data (a); differences of HD (b) and SD (c) over the entire Jing-Jin-Ji area between the ensemble mean and observation

Table 1 The correlation coefficient, mean error and root mean square error of HD and SD over the entire Jing-Jin-Ji area between the simulations and observation

Fig. 2 Spatial distribution of the present day (1986-2005) and spatial distribution of the future change of hazard factor over the Jing-Jin-Ji district compared with the present day

Fig. 3 Future change of hazard factor over the Jing-Jin-Ji district compared with the present day (a, b), and regional average value of GDP and population (c) (In (c), blue star indicates the time point when the regional average value exceeds 1 time of average value of the reference period in the process of GDP rising, and red star indicates the time point when the regional average value is lower than the value of reference period in the process of population falling)

Fig. 4 Spatial distribution of GDP and population in the present day and the change of future periods in the 21st century over the Jing-Jin-Ji district

Fig. 5 Spatial distribution of GDP and population exposure in the present day and the change of future periods in the 21st century over the Jing-Jin-Ji district

Table 2 Regional mean changes of GDP/population exposures ratio compared with the present day (1986—2005) over some cities of Jing-Jin-Ji district, respectively

Fig. 6 Regional mean GDP/population exposures over cities of Jing-Jin-Ji district, respectively

Table 3 Analysis of the influencing factors of population exposure and GDP exposure to high temperature in Jing-Jin-Ji district

Fig. 7 Contribution rates of influencing factors of GDP/population exposures in cities of Jing-Jin-Ji district during three future periods

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