Integrate Risk From Climate Change in China Under Global Warming of 1.5 and 2.0 °C.
Abstract.
Risk of climate-related impacts results from the interaction of climate-related hazards (including hazardous events and trends) with the vulnerability and exposure of human and natural systems. Despite the commitment of the Paris Agreement, the integrate research on climate change risk combining risk-causing factors and risk-bearing bodies, the regional differences in climate impacts are still missing. In this paper we provide a quantitative assessment of hazards and socioeconomic risks of extreme events, risks of risk-bearing bodies in China under global warming of 1.5 and 2.0 °C based on future climate scenarios, and quantitative evaluation theory for climate change risk. For severe heat waves, hazards might significantly intensify. Affected population under 2.0 °C warming might increase by more than 60% compared to that of 1.5 °C. Hazards of severe droughts and floods might strengthen under Representative Concentration Pathway 8.5 scenario. Economic losses might double between warming levels of 1.5 and 2.0 °C, and the population affected by severe floods might continuously increase. Under the integrate effects of multiple disasters, the regions with high population and economic risks would be concentrated in eastern China. The scope would gradually expand to the west with socioeconomic development and intensification of extreme events. High ecological risks might be concentrated in the southern regions of the Yangtze River Basin, while the ecological risk in northern China would expand. High agriculture yield risks might be distributed mainly in south of the North China Plain, the Sichuan Basin, south of the Yangtze River, and west of Northwest China, and the risk levels might continuously increase.
Plain Language Summary.
Based on the climate and socioeconomic scenario data from the Inter-Sectoral Impact Model Inter-comparison Project and International Institute for Applied Systems Analysis, risks from climate change were identified under different warming levels, considering socioeconomic damage and ecosystem and food production losses. The relatively developed areas of eastern China exhibit high risks, and the risks show a westward and northward expansion trend. Socioeconomic damage, natural ecosystem, and food production losses would increase with socioeconomic development and intensification of global warming. We believe that a quantitative assessment of climate change risk should integrate climate-related hazards with the vulnerability and exposure of human and natural systems. These results can provide decision-making support for governments and a database for adapting to climate change.
1 Introduction.
Risk of climate-related impacts results from the interaction of climate-related hazards (including hazardous events and trends) with the vulnerability and exposure of human and natural systems (Intergovernmental Panel on Climate Change (IPCC), 2014a, 2018). The Paris Agreement committed to controlling the increase in global mean surface temperature (GMST) to “well below 2.0 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels” (UN Framework Convention on Climate Change (UNFCCC), 2015). Even if the nationally determined contributions promised in the Paris Agreement are completely realized, the GMST would increase to 2.8 °C above preindustrial levels by 2100 (Climate Action Tracker, 2017; Raftery et al., 2017). In the context of climate change, the intensity and frequency of extreme climate-related events (e.g., droughts, heat waves, and floods) would increase rapidly and nonlinearly, with increasingly severe impacts (Donnelly et al., 2017; Fischer & Knutti, 2015; Liu et al., 2018). The frequency of extreme high temperatures under a GMST increase of 2.0 °C is almost double that under an increase of 1.5 °C and more than 5 times higher than for present conditions. The duration, intensity, and impacts of droughts in most regions are lower under a GMST increase of 1.5 °C than under a GMST increase of 2.0 °C. Extreme precipitation events exhibit great regionality, and the duration and impacts of floods in most areas would increase with global warming.
Climate change has caused temperature increases, precipitation changes, increased melting of glaciers, imbalances in hydrology and water resources, and frequent extreme weather events (Qin, 2015). These conditions pose risks to population, economy, natural ecosystem, and food production (Milly et al., 2002; Scholze et al., 2006). In the future, extreme precipitation would increase in monsoon regions, and the affected area and population would exhibit nonlinear increasing trends (Zhang et al., 2018). The population and economic losses caused by floods would be relatively large in developing countries (Lim et al., 2018). Currently, disasters are frequent in China, affecting large areas and causing great losses. By the end of this century, the North China Plain might become uninhabitable due to extreme heat waves caused by climate change and the development of irrigation agriculture (Kang & Eltahir, 2018). The direct economic losses due to droughts in China would increase several tens of billions of U.S. dollars when the GMST increases by 2.0 °C compared with 1.5 °C warming (Su et al., 2018). The flood risk areas in China are expanding, and population and economic exposure would continue to increase (Li et al., 2012). Climate change severely affects natural ecosystems and food production (Bebber et al., 2013; Piao et al., 2010). The global wheat production is estimated to fall by 6% for every 1 °C of further temperature increase (Asseng et al., 2015). Approximately 30% of ecosystems in China would be vulnerable under Representative Concentration Pathway (RCP8.5) scenario (Gao et al., 2018).
The assessments of previous climate risk research have focused mainly on single risk-causing factors or risk-bearing bodies. These studies have included quantitative evaluations of socioeconomic losses due to droughts, floods, and heat waves in the context of climate change (Nangombe et al., 2018; Sun et al., 2014) and climate change risk assessments of natural ecosystems and food production (Challinor et al., 2014; Gao et al., 2017; Urban, 2015). Under the influence of climate change, however, the frequency and intensity of natural disasters have increased. The chain reactions and concurrency of multiple disasters have had a combined effect on risk-bearing bodies. In 2018, large-scale heat waves occurred in eastern China. Torrential rains, floods, and periodic droughts occurred in the middle reaches of the Yangtze River, and repeated disasters caused serious damage to the local social and economic systems. Integrate classification and corresponding methodological studies of risk-causing factors and risk-bearing bodies in climate change risk research are critical, and the conclusions of these risk assessments can be normalized to ensure that they are comparable with defined application directions. This method will provide a foundation for the realization of the sustainable development goals of the UN, the reduction and management of climate change risk, the mitigation of climate change, and the implementation of orderly adaptations (Jones & Preston, 2011; Ploberger & Filho, 2016; UN, 2015; Wu et al., 2018).
This study assessed the hazards of extreme events in China and evaluated the risk of extreme events and the integrate risk to the population, economy, ecosystems, and food production. The scenarios considered include global warming of 1.5 and 2.0 °C. These scenarios were based on bias-corrected daily output results from global climate models (GCMs) and the quantitative evaluation theory for climate change risk. The results are expected to provide guidance for adapting to climate change and responding to extreme weather and climate events, establishing a disaster management system and aiding the sustainable development of the national economy.
2 Materials and Methods.
2.1 Data Sources.
A monthly climate data set from 29 GCMs under the Coupled Model Inter-comparison Project Phase 5 driven by multiple RCP scenarios was used to calculate the GMST and mean surface temperature over China (CMST), and the periods for different warming targets were determined for the world and China (Table S1 of the Supporting Information; Van Vuuren et al., 2011; Taylor et al., 2012). A daily climate data set with a spatial resolution of 0.5° × 0.5° from 1950 to 2099 was simulated by five models under the framework of the Inter-Sectoral Impact Model Inter-comparison Project (Warszawski et al., 2014). The climate projections of these five models can represent the ranges of climate change and the corresponding impact results of the other Coupled Model Inter-comparison Project Phase 5 models well (Huang et al., 2017). The data set was bias-corrected with WATCH forcing data (Hempel et al., 2013; Weedon et al., 2011) and used to calculate the extreme events index and classify hazards. This paper selected RCP8.5 scenarios, which represent scenarios of maximum greenhouse gas emissions (Moss et al., 2010).
Socioeconomic scenario data, including population and GDP values, which were derived from Shared Socio-economic Pathways (SSPs; O'Neill et al., 2014) and downscaling scenario data sets, were simulated by the National Institute for Environmental Studies, Japan, based on the SSP database of the International Institute for Applied Systems Analysis. The data interval was every 10 years from 1980 to 2100, and the spatial resolution was 0.5° × 0.5° (Murakami & Yamagata, 2016).
Global average temperature series from the Met Office Hadley Centre and Climatic Research Unit (Morice et al., 2012) and CMST series calculated by Tang et al. (2009) were used to verify the capability of the model to simulate global climate and the regional climate in China. Land use data obtained from the Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences were used to determine the major agricultural regions (http://www.resdc.cn/data.aspx? DATAID = 184; Ning et al., 2018).
2.2 Analysis Methods.
2.2.1 Calculation of Threshold Years for Global Warming of 1.5 and 2.0 °C.
According to the UN Framework Convention on Climate Change (UNFCCC), a threshold year refers to a year in which the GMST rises by a certain amount above preindustrial levels. We selected the global warming of 1.5 and 2.0 °C based on the objectives of the Paris Agreement. Considering the different start time of the models and global warming in the twentieth century, 1861–1890 was selected as the reference period for calculating the threshold years in the 21st century. The anomaly sequence of each model and the multimodel ensemble relative to the reference period were calculated. In order to eliminate the influence of interannual variation characteristics of climate, the 5-year moving average processing of the GMST was carried out, and the time when it initially exceeded 1.5 °C (2.0 °C) was chosen as the year of warming appearance. Steffen et al. (2018) found that if a planetary threshold (around 2.0 °C above preindustrial temperature) is exceeded, it will cause continued warming on a Hothouse Earth pathway. Therefore, we chose the 30 years with the threshold year as the last year as the warming period of 1.5 °C (2.0 °C; Table S2 of the Supporting Information). To calculate the ensemble mean, the model results were interpolated to a 2.5° × 2.5° grid, and the area-weighted mean method was conducted (Jones & Hulme, 1996; Zhou & Yu, 2006). The error bar of the GMST anomalies sequence was expressed by 95% confidence intervals.
The multimodel ensemble mean shows that the warming rate in China is significantly faster than the global mean under RCP8.5 scenarios (Figure 1). Currently, the GMST is approximately 1 °C above preindustrial levels (Morice et al., 2012), and the CMST will reach 1.5 °C above preindustrial levels in approximately 2020. In the subsequent analysis, this study defines this period as the current climate. It is estimated that GMST would reach 1.5 °C warming above preindustrial levels (1861–1890) by approximately 2026. The CMST might reach 1.8 °C above preindustrial levels at these time. The GMST might reach 2.0 °C warming above preindustrial levels by approximately 2040, during which the CMST will reach 2.5 °C above preindustrial levels. These results are basically consistent with the results of previous studies (Karmalkar & Bradley, 2017; Nikulin et al., 2018; Su et al., 2018).
2.2.2 Climate Change Risk Assessment Theory.
Climate change risk originates from interactions between climate-related hazards and the exposure and vulnerability of human and natural systems. Risk composition consists of two dimensions (i.e., risk-causing factors and risk-bearing bodies) and three aspects (i.e., probability, vulnerability, and exposure; Figure 2; Wu et al., 2011; IPCC, 2014a). In climate change risk research, risk-causing factors, which include natural climate and anthropogenic climate change, determine the probability of risk occurrence and are presented as sudden onset events and slow onset events (IPCC, 2007). Risk-bearing bodies are socioeconomic and resource environments that experience negative effects, including humans, livelihoods, environmental services and various resources, infrastructure, and economic, social, or cultural assets (Jones, 2004). Exposure and vulnerability are two attributes of risk-bearing bodies; the former refers to the number of risk-bearing bodies that may experience adverse effects, and the latter refers to the tendency or trend of adverse effects, which is often characterized by sensitivity and propensity (IPCC, 2012; Wu, Gao, et al., 2018).
This study determined assessment methods from different sources of climate change risk: (1) Sudden onset events, that is, extreme weather/climate events, which occur within a short period of time and are associated with hazards and adverse consequences. Climate change factors are equivalent to disaster-causing factors (risk-causing factors) in natural disasters (Li et al., 2012; Liu et al., 2018). (2) Slow onset events, which occur when system indicators exceed certain thresholds, causing adverse effects and risks (Rosenzweig et al., 2013; Yin et al., 2018). The reference period for the climate change risk assessment was from 1961 to 1990.
2.2.2.1 Risk Assessment of Sudden Onset Events.
(1)- Hazards of extreme events.
(2)| Index | Mild | Moderate | Severe | Affected region |
|---|---|---|---|---|
| CI | −1.8 < CI ≤ −1.2 | −2.4 < CI ≤ −1.8 | CI ≤ −2.4 | Agricultural economy |
| HI | 2.8 ≤ HI < 6.5 | 6.5 ≤ HI < 10.5 | HI ≥ 10.5 | Population |
| FI | 30(35)–150 mm | 150–250 mm | ≥250 mm | Population and economy |
where HE,i represents the frequency of extreme events, set to 1 if the frequency is greater than 100%, that is, nE,i ≥ T; nE,i is the number of extreme events; i indicates the level of extreme events; and T refers to the warming period.
- Exposure of risk-bearing bodies.
- Vulnerability of risk-bearing bodies.
To explore the relationship between floods and precipitation, statistical data from 1001 flood disasters from 1990 to 2008 were collected from the China National Commission for Disaster Reduction. The quantitative relationship, that is, vulnerability curve of flood disasters, between different flood magnitudes and their corresponding losses (e.g., affected population and economic losses) was established according to the classification criterion of the maximum accumulated 3-day precipitation in central regions (Figure 3). This relationship was used to construct the loss criteria for different flood magnitudes in China (Li et al., 2012). Historical data of drought disasters were collected from China drought disaster data set from 1949 to 1999. The disaster-loss fitting method was used to assess the agricultural loss rate under different levels of drought (He et al., 2013; Xu et al., 2013). Heat waves have known impacts on populations, and almost all members of a population will be exposed when a heat wave disaster occurs. Therefore, the affected population rate was set to 100%.
Hazards, exposure, and vulnerability were combined to assess the risk of sudden onset events in the context of climate change.
2.2.2.2 Risk Assessment of Slow Onset Events.
- Ecosystem risk.
- Food production risk.
(3)
(4)3 Results.
3.1 Hazards of Extreme Events.
Considering that changes in severe extreme events will become more significant under climate change scenarios and that requirements for adapting to climate change will increase, the analyses in the following section will focus mainly on hazards of severe extreme events and their risk distribution and dynamic changes. At present, severe droughts, which occur in most regions of China, except for the west of the Tibetan Plateau and the west of the Inner Mongolian Plateau, are concentrated mainly in the Middle-Lower Yangtze Plain and the southern part of Central China (Figures 4a and S2 of the Supporting Information). The current average hazard index of severe droughts is 0.0619 (Table 2). Severe heat waves are located mainly in flat plains and basins, such as the Junggar Basin, the Tarim Basin, the North China Plain, and Central China (Figure 4d). The current average hazard index of severe heat waves is 0.2221 (Table 2). Severe floods are distributed mainly in South China, the southeastern part of the Tibetan Plateau, the southern part of Central China, and Northeast China (Figure 4 g). The average hazard index of severe floods is 0.0244 (Table 2).
| Extreme events | Current | 1.5 °C | 2.0 °C |
|---|---|---|---|
| Drought | 0.0619 | 0.0655 | 0.0693 |
| Heat wave | 0.2221 | 0.2828 | 0.3448 |
| Flood | 0.0244 | 0.0242 | 0.0260 |
For the period when the GMST exceeds 1.5 °C, the average hazard indexes for severe droughts would increase, and the hazard index would decrease in the Middle-Lower Yangtze Plain (Figure 4b and Table 2); the trend of the change in distribution area would not be obvious during this period (Figure 5a). The average hazard indexes for severe heat waves would increase significantly, especially in the western part of Inner Mongolia, the North China Plain, and Central China. The distribution area of severe heat waves would first increase and then decrease during this period (Table 2 and Figures 4e and 5b). The average hazard indexes for severe floods would decrease slightly, and the main distribution area would remain generally consistent with the distribution under the current climate; the distribution area would be no significant change (Table 2 and Figures 4 h and 5c).
When the GMST increase is 2.0 °C, the average hazard indexes for severe droughts would increase significantly, especially in the southeastern part of the Loess Plateau, the Middle-Lower Yangtze Plain, and Central China (Figure 4c and Table 2); the trend of the change in distribution area would increase weakly (Figure 5a). The average hazard indexes for severe heat waves would further increase, particularly in the North China Plain, Central China, and the western part of South China. The distribution area of severe heat waves would increase significantly during this period (Table 2 and Figures 4f and 5b). The average hazard indexes for severe floods would increase mainly in the northern part of Central China (Figure 4i and Table 2); the distribution area of severe floods would increase during this period (Figure 5c).
3.2 Risks of Extreme Events.
The socioeconomic risks of extreme events, that is, droughts, heat waves, and floods, were calculated based on climate and socioeconomic scenario data under global warming of 1.5 and 2.0 °C.
Droughts mainly affect agriculture. The economic losses from droughts in major agricultural regions were assessed by assuming that the percent contribution of the total agricultural production to GDP will remain unchanged in the future in all regions of China. The major agricultural regions of China are distributed in the Sanjiang Plain, the Northeast Plain, the eastern part of the Loess Plateau, the Guanzhong Plain, the North China Plain, the Middle-Lower Yangtze Plain, and the Sichuan Basin; the other agricultural regions are relatively scattered throughout China (Figure S1 of the Supporting Information). Currently, more than 90% of the major agricultural regions of China are damaged by severe droughts, and the economic losses exceed U.S. $7 billion (Table 3 and Figure 6a). Among these regions, the North China Plain and the Middle-Lower Yangtze Plain will have the largest losses, followed by the eastern part of the Loess Plateau, the Guanzhong Plain, and the Sichuan Basin. The Sanjiang Plain and the Northeast Plain have the least losses. The economic losses from droughts in the major agricultural regions would show a nonlinear increasing trend with increasing temperature in the future. Specifically, the economic losses from droughts would increase by approximately 130% in a 1.5 °C warming world compared with the current climate situation (Table 3 and Figure 6b), and the annual economic losses would show a significant increasing trend (Figure 7a). With the GMST increase of 2.0 °C, drought losses would increase by approximately 73% relative to global warming of 1.5 °C (Table 3 and Figure 6c), and the annual economic losses would continue to increase (Figure 7a).
| Risks | Current | 1.5 °C | 2.0 °C |
|---|---|---|---|
| Economic losses from drought | 7.48 | 17.20 | 29.67 |
| Economic losses from floods | 12.20 | 33.03 | 65.99 |
| Population affected by heat waves | 255.7 | 404.6 | 660.0 |
| Population affected by floods | 9.13 | 11.21 | 13.20 |
- Note. Loss units: U.S. $ billion; population units: million.
Heat waves have negative impacts on populations. This study hypothesized that the affected population rate would be 100%, and this rate was used to evaluate the impacts of heat waves on the population in the future. Currently, severe heat waves occur in approximately half of the regions of China and affect more than 20% of the total population (Table 3 and Figure 6 g). The impacted areas are distributed mainly in the southern part of North China, East China, the Sichuan Basin, and South China. With the GMST increase of 1.5 °C, the regions that are impacted by severe heat waves would expand to the Inner Mongolian Plateau and the Tarim Basin, covering more than 60% of the total land area in China and affecting nearly 30% of the population (Table 3 and Figure 6 h). The annual affected population would show a significant increasing trend (Figure 7c). With the GMST increase of 2.0 °C, severe heat waves would affect approximately 80% of the total land area in China and nearly 50% of the population. The impacted regions would occur throughout China, except for in the Tibetan Plateau and Changbai Mountains (Table 3 and Figure 6i). The annual affected population would increase more significantly (Figure 7c).
Floods result in serious economic losses and impacts on the population, and this study predicted the impacts of floods on the economy and population in the future based on historical statistical data of flood disasters. Currently, the economic losses from severe floods are approximately U.S. $12.20 billion and affect more than 9 million people (Table 3). The impacted areas are distributed mainly in South China, the northern part of East China, the southern part of Central China, the southern part of Northeast China, and southern Tibet (Figures 6d and 6j). For the period when the increase in the GMST exceeds 1.5 °C, the economic losses from severe floods would be nearly 3 times the losses under the current climate situation, and the affected population would exceed 11 million (Table 3). The impacted areas would be generally consistent with the current climate situation (Figures 6e and 6 k). The annual economic losses and affected population will not be obvious (Figures 7b and 7d). For the period when the increase in the GMST exceeds 2.0 °C, the direct economic losses would be approximately 2 times greater than those under 1.5 °C warming world, and the affected population would exceed 13 million (Table 3 and Figures 6f and 6 l). The annual economic losses and affected population would show significant increasing trends (Figures 7b and 7d).
3.3 Integrate Risks of Risk-Bearing Bodies.
The integrate risks of risk-bearing bodies, that is, population, economy, natural ecosystem, and food production, were assessed in accordance with the ultimate objectives of the UNFCCC (Griggs et al., 2013; UNFCCC, 1992).
The population risk was obtained by combining the population affected by heat waves and floods. The population distribution in China is roughly bounded by the Heihe-Tengchong Line, with more people living on the southeastern side of the line and fewer people living on the northwestern side of the line. In the future, regions with a high population risk would be distributed mainly in the southeastern region of China. Currently, the regions with a high population risk are concentrated mainly on the North China Plain, Central China, East China, South China, the Sichuan Basin, and the central part of the Northeast Plain (Figures 8a). With 1.5 °C of warming, the distribution of regions with a high population risk would be significantly expanded, particularly on Northeast China, the Loess Plateau, Southwest China, and both sides of the Tianshan Mountains (Figures 8b). With 2.0 °C of warming, the distribution of regions with a high level of population risk would be generally consistent with the distribution when the GMST increases by 1.5 °C, with expansion in Northeast China, the southern part of the Tibetan Plateau, and the southern part of Southwest China (Figures 8c).
The economic losses from droughts and floods are combined into the category of economic risk. Due to regional differences in development, Chinese economy is distributed mainly in the eastern and central parts of the country, and regions with high levels of economic risk would be concentrated in these regions in the future. Under the current climate, the regions with high levels of economic risk are mainly concentrated on the North China Plain, the northern part of East China, Central China, South China, the western part of the Sichuan Basin, and the southern part of Northeast China (Figures 9a). With 1.5 °C of warming, the distribution of regions with a high level of economic risk would expand significantly westward, especially in the northern part of Northwest China and Southwest China under both scenarios (Figures 9b). With 2.0 °C of warming, the distribution of areas with a high level of economic risk would be generally consistent with the distribution when the GMST increases by 1.5 °C (Figures 9c).
The multiyear averaged NPP in China varied spatially and gradually decreased from southeast to northwest during the reference period. Currently, the regions with a high level of ecological risks are mainly concentrated on the Yangtze River Basin, Southwest China, the southern part of East China and the eastern part of South China, southern Tibet, and Hulunbuir (Figures 10a). With 1.5 °C of warming, the spatial patterns of the regions with a high level of ecological risk would remain basically unchanged. The risk levels would increase in South China, Hulunbuir, and the northeastern part of the Tibetan Plateau (Figures 10b). With 2.0 °C of warming, the risk levels would further increase in the southern part of Central China, the central and western part of Northeast China, and the northeastern part of the Tibetan Plateau, while the risk levels would decrease in the middle and lower reaches of the Yangtze River (Figures 10c).
During the current climate period, the regions with a high level of food production risk are mainly concentrated on southern part of the North China Plain, the Sichuan Basin, southern part of East China, southern part of Southwest China and South China, the northern part of Northeast China, and the western part of Northwest China (Figures 11a). With 1.5 °C of warming, the distribution of regions with a high level of food production risk would increase in the northern and southern part of the Yangtze River and decrease slightly in the northern part of Northeast China (Figures 11b). With 2.0 °C of warming, the distribution of regions with a high level of food production risk would increase in the southern part of the Yangtze River and the central portion of the Northeast Plain and decrease slightly in the northern portion of Northeast China and the Sichuan Basin (Figures 11c).
4 Discussion.
This research selected three extreme events that had wide distributions and were vulnerable to climate change to quantitatively assess the spatial patterns of hazards under different periods of global warming. Other extreme events that may be associated with climate change, such as cold, typhoons, snowstorms, and storm surges, were not considered in this study due to their regional characteristics. Extreme events are one of the main routes by which climate change causes socioeconomic risks, which are generally manifested as population impacts, food production losses, housing damages, and economic losses. In this study, economic losses included not only agricultural losses but also some extreme events such as floods, which may also cause economic losses to secondary and tertiary industries. This paper primarily assessed the spatial patterns of economic losses due to droughts, population risks of heat waves, population, and economic risks of floods. The risk-bearing bodies of the population, economy, natural ecosystem, and food production were used to assess the integrate risk of climate change, which is in accordance with the ultimate objectives of the UNFCCC. Future climate change risk studies can increase the number of risk-bearing bodies based on different goals and regions.
The results in this paper found that the spatial patterns of climate change risks show significant spatial and temporal heterogeneity. Because the risks include risk-causing factors and the risk-bearing bodies, as a result, even if the eastern bears as much climate change as the western (or even less), it would face a higher risk. The rapid socioeconomic development in eastern China, coupled with the frequent occurrence of extreme events and high vulnerability, would lead to a high level of climate change risk in the future. The region includes Beijing-Tianjin-Hebei Collaborative Development Zone, where climate change risks mainly come from the social economy, and Yangtze River Economic Belt, where climate change risks mainly come from the social economy, ecology system, and food production. The implementation of national regional development strategies should take into account regional differences of future climate change and identify risks and possible impacts of climate change. Adaptation to climate change moderates impacts and risks of climate change by reducing vulnerability. We take East and South China as an example to discuss adaptation measures. East and South China includes provincial administrative regions of Shandong, Jiangsu, Anhui, Shanghai, Zhejiang, Jiangxi, Fujian, Taiwan, Guangdong, Hong Kong, Macao, Guangxi, and Hainan. Under RCP8.5 scenario and global warming of 1.5 °C, according to formula 1, affected population (units: million) and economic losses (units: U.S. $ billion) of severe floods would be 10.18 ≈ 0.050 × 73,135.95 × 0.280 and 30.56 ≈ 0.050 × 9,467.84 × 0.065. With 2.0 °C of warming, affected population and economic losses would be 11.95 ≈ 0.056 × 76,714.56 × 0.280 and 60.58 ≈ 0.056 × 16,513.94 × 0.065, respectively. To reduce the risk to the level of 1.5 °C warming, population and economic vulnerability would be 0.237 and 0.033; that is, 15% of the population and 49% of the economic vulnerability need to be reduced.
Climate change risk theory (Figure 1) states that effective methods for reducing climate change risk include decreasing excessive warming by mitigating anthropogenic climate change, reducing extreme events, and enhancing the adaptability and resilience of risk-bearing bodies (IPCC, 2014a; IPCC, 2014b). In addition, climate change risk shows significant regional characteristics that should be used as a basis to formulate countermeasures for adaptation (Russel et al., 2018; Wu et al., 2017). Based on the above quantitative assessment and with reference to the National Strategy on Adaptation to Climate Change, the impacts of and key tasks for regional adaptation to climate change in China are summarized in Table 4.
| Region | Impacts | Key tasks |
|---|---|---|
| Northeast China, North China, and Northwest China | Climate warming, water resource shortages, ecological fragility, and increasing agricultural risk | Improve water resource management, develop water-saving agriculture, strengthen ecological construction and restoration, rationally distribute the integration of Beijing-Tianjin-Hebei, and strengthen desertification management |
| East China, Central China, and South China | Climate warming, rising sea levels, frequent extreme weather/climate events, threats to food production, and ecosystems | Enhance disaster prevention and mitigation capabilities, adjust agricultural configurations, strengthen ecological conservation, and adjust the development of urban agglomerations |
| Southwest China | Complex climate, highly vulnerable ecological environment, and threats to food production | Strengthen comprehensive remediation of mountain disasters and rocky desertification, conserve biodiversity, and ensure food security |
| Tibetan Plateau | Severe water resources and ecological security | Enhance water resource management and ecological conservation, demonstrate regional advantages, and reduce climate change risk |
The fundamental principles of climate change risk can be summarized as futurity, disadvantage, and uncertainty. These findings reveal the essential characteristics of climate change risk from the perspectives of time, consequences, and the representation of consequences (Wu et al., 2011). Quantitative risk assessment is the quantification of possible losses encountered by systems in the future based on an assessment of the uncertainty of impacts. Climate models and emission scenarios are usually based on global scales. Uncertainty may arise in the process of downscaling to the scale of China. The uncertainty in climate change risk prediction arises from the uncertainties in the future climate and socioeconomic scenarios. The vulnerability of risk-bearing bodies is the primary focus for the quantitative evaluation of climate change risk, and there is a degree of uncertainty in the current research on vulnerability. These factors, to some extent, all contribute to the uncertainty in climate change risk assessment. In order to improve the reliability of quantitative assessment climate change risk, it is necessary to improve the accuracy of GCMs simulation. We can more accurately predict climate change and the exposure of risk-bearing bodies only by fully considering future global and regional socioeconomic development. Regional parametric correction and the pertinence of the vulnerability of risk-bearing bodies will be enhanced with the improvement of disaster statistics and the quantification of response measures. As research on the relationship between the magnitudes of risk-causing factors and the vulnerability of risk-bearing bodies increases and model simulations continue to improve, the uncertainty in vulnerability curves will decrease.
5 Conclusions.
- Under RCP8.5 scenarios, GMST would increase to 1.5 °C above preindustrial levels in 2026 and 2.0 °C in 2040, respectively. The hazards of severe extreme events would gradually increase under RCP8.5 scenario. With 2.0 °C of warming, severe droughts, heat waves, and floods will occur in Central China, the Tarim Basin and the North China Plain, southern Tibet, and the southeast coast every 5, 1, and 4 years, respectively. For the national average, the return periods will be 14, 3, and 38 years, respectively.
- Droughts would affect the major agricultural regions in the Yangtze River Basin and its northern regions. Heat waves would affect most regions in China, with the exception of the Tibetan Plateau and northern mountainous areas. Floods would mainly affect the population and economy of the eastern part of China, particularly the coastal regions. Economic losses from severe droughts and floods might double between 1.5 and 2.0 °C warming. The affected population from severe heat waves and floods would continuously increase. The annual socioeconomic losses would increase significantly between 1.5 and 2.0 °C warming.
- Risk-bearing bodies of population and economy are affected primarily by heat waves and floods, droughts, and floods, respectively. Regions with high risk levels would be concentrated mainly in eastern China. The distribution of risk would gradually expand westward with social development and temperature increases. Regions with a high level of ecological risks would be primarily concentrated in the Yangtze River Basin and its southern regions, and the risk levels in northeastern China would increase significantly. The regions with a high level of food production risks will be primarily distributed in the western part of Northwest China, southern part of the North China Plain, the Sichuan Basin, and southern part of the Yangtze River. The grain yield would continue to decrease as the temperature increases.
Acknowledgments
This research was financially supported by the National Key Research and Development Program of China (Grant 2018YFC1509002), the Key Program of National Natural Science Foundation of China (Grant 41530749 and 41671098), the National Key Research and Development Program of China (Grant. 2018YFC1508801), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19040304). Climate scenario data were collected and available at the Earth System Grid Federation data node at the Lawrence Livermore National Laboratory (https://esgf-node.llnl.gov/search/esgf-llnl/). Socioeconomic scenario data were retrieved from the Center for Global Environmental Research, National Institute for Environmental Studies (http://www.cger.nies.go.jp/gcp/population-and-gdp.html). Global mean surface temperature data were obtained from the Met Office Hadley Centre and the Climatic Research Unit at the University of East Anglia (http://www.cru.uea.ac.uk/). Mean surface temperature of China data from Tang et al. (2009) were available and cited in the text. Land use data are available from the Resources and Environmental Sciences Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/data.aspx? DATAID = 184). The disaster statistics data were provided by China National Commission for Disaster Reduction (http://www.ndrcc.org.cn/zqtj/index.jhtml). The authors acknowledge all the groups for producing and sharing their data set.
