Yuyu Ren

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market.

Land surface air temperature in Asia has been increasing significantly since the 1950s. However, current understanding of Asian warming since 1901 in terms of observations and simulations is still poor. Based on a newly developed observation dataset with 2658 stations and Coupled Model Intercomparison Project Phase 5/6 (CMIP5/6) output data, we analyze changes in mean (T_mean), maximum (T_max), and minimum (T_min) temperature, and diurnal temperature range (DTR) over Asia during 1901-2100. Annual mean land surface air temperature over Asia increased significantly, and T_mean, T_max, and T_min increased by 1.81, 1.47, and 2.15°C during 1901-2020, respectively. The T_min warming rate is about 1.5× that of T_max, resulting in a decline of Asian DTR by 0.68°C since 1901. We also found that Asia has experienced more substantial warming than the global case and the Northern Hemisphere, and the decline in DTR is more substantial in Asia. Spatially, Asia exhibits a general warming trend with a gradual increase in spatial warming from low to high latitudes, and the effect of high-latitude warming has gradually strengthened since the 2000s. Seasonally, Asian warming in the cold season is stronger than in the warm season. Furthermore, CMIP5/6 can capture the Asian warming in the historical period 1901-2020. However, it underestimated T_min and overestimated T_max, contributing to their poor performance in simulating the historical change of DTR. Under Shared Socioeconomic Pathway (SSP) 2-4.5, Asian T_mean, T_max, and T_min are projected to increase by 3.5, 3.4, and 3.7°C century^-1 during 2021-2100, respectively. The Asian warming rate under SSP5-8.5 is about 2× that of SSP2-4.5.

Much attention has been given to the change rule of a single extreme event, and there are few reports on comprehensive characteristics of multiple extreme events in a certain region. Based on the analyzes of annual frequency of extreme high temperature, extreme low temperature, extreme drought, extreme precipitation, and extreme typhoon events in China from 1961 to 2020, a multi extreme events composite risk grade index (MXCI) was constructed and applied to the comprehensive characteristics analyzes of multiple extreme events in China. The results show that the high value areas of MXCI were mainly located in southeast China and southwest China. Over the past 60 years, the MXCI has shown a decreasing trend in western China and most of southeastern China, and an increasing trend in the middle zone from southwest China to northeast China. Through comparative analysis, MXCI can objectively reflect the comprehensive characteristics of multiple extreme climate events in a region, which is helpful to understand regional extreme climate characteristics and effectively cope with extreme climate risks.

Based on the daily observation data of 2254 meteorological stations in China from 1961 to 2021 after homogenization and correction, the extreme value analysis method that combines relative threshold and absolute threshold was used to identify extreme climate events in the mainland of China, namely extreme high temperature, extreme low temperature, extreme drought, extreme precipitation and extreme typhoon events. The frequency and linear variation trends of individual extreme events in the past 60 years were investigated. On this basis, the multi extreme events composite risk grade index (MXCI) was constructed, which can objectively reflect the comprehensive characteristics of multi extreme climate events in different regions. The results show that the high value areas of MXCI were mainly located in Southeast China and Southwest China. The MXCI presented a decreasing trend in most of the western and southeastern regions of China, while an increasing trend from Southwest China to the south of Northeast China, due to the frequent occurrence of extreme high temperature, extreme drought and other extreme events in these regions.

Based on the homogenized daily data of 2255 meteorological stations during the past 60 years from 1961 to 2020, the potential evapotranspiration was calculated using the revised FAO56 Penman–Monteith model, and then the annual AI (aridity index, the ratio of annual potential evapotranspiration to annual precipitation) was employed to analyze the dry-wet climate change in China. The GCM models’ prediction data was used to analyze the possible trends of dry-wet climate in China by the end of this century. The results showed that in the past 60 years, the climate in China was getting wetter, especially in the western regions of China, including Xinjiang, western Qinghai, Gansu, western Inner Mongolia, and northwestern Tibet. In the last 10 years, China’s climate has become more humid. Compared with the 1960s, the total area of aridity has decreased by about 650,000 square kilometers. The changes of different climate zones have regional and periodical characteristics. There was a tendency to get wet periods in all four seasons, especially in summer. Analysis of GCM model projection data shows that by the end of this century, the climate in China would have a general trend of becoming drier. The drier regions are mainly located in the central and eastern parts of China, while the western regions of China continue to maintain the wetting trends. In the case of high emissions, the trends of drying in the central and eastern and wetting in the west are more significant than in the case of medium emission.