Impacts of Anthropogenic Forcings and El Niño on Chinese Extreme Temperatures
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This study investigates the potential influences of anthropogenic forcings and natural variability on the risk of summer extreme temperatures over China. We use three multi-thousand-member ensemble simulations with different forcings (with or without anthropogenic greenhouse gases and aerosol emissions) to evaluate the human impact, and with sea surface temperature patterns from three different years around the El Niño–Southern Oscillation (ENSO) 2015/16 event (years 2014, 2015 and 2016) to evaluate the impact of natural variability. A generalized extreme value (GEV) distribution is used to fit the ensemble results. Based on these model results, we find that, during the peak of ENSO (2015), daytime extreme temperatures are smaller over the central China region compared to a normal year (2014). During 2016, the risk of nighttime extreme temperatures is largely increased over the eastern coastal region. Both anomalies are of the same magnitude as the anthropogenic influence. Thus, ENSO can amplify or counterbalance (at a regional and annual scale) anthropogenic effects on extreme summer temperatures over China. Changes are mainly due to changes in the GEV location parameter. Thus, anomalies are due to a shift in the distributions and not to a change in temperature variability.
Key wordsextreme temperatures ENSO anthropogenic impact climate risk
本研究探讨了人为强迫和自然变率对中国夏季极端高温灾害的潜在影响. 我们使用了不同强迫条件下(包括或者不包括温室气体和气溶胶排放)的三千多个成员集合模拟结果, 来评估人为强迫的影响;同时, 利用最近一次ENSO事件发展演变过程中的三个不同位相年份(2014中性年、2015厄尔尼诺年、2016拉尼娜年)对应的海表温度型态来评估自然变率的影响. 我们利用广义极值分布来分析集合结果. 基于模式结果, 我们发现在ENSO峰值期间(2015年), 日间极端气温在中国中部地区偏小. 在2016年, 夜间极端高温灾害在中国东部沿海地区大幅增加. 上述二者(自然变率的影响)都与人为影响的量级相当. 因此, 我们认为ENSO事件(在区域和年际尺度上)能够放大或者抵消人为强迫对中国夏季极端高温的影响. 此外, 本研究揭示了中国夏季极端高温的变化主要取决于广义极值分布参数的变化, 这意味着中国夏季极端高温的变化是由温度极值分布的偏移造成的, 而非温度变率本身强度的变化.
关键词极端高温 厄尔尼诺-南方涛动 人为影响 气候灾害
This work and all contributors were supported by the UK–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. We would like to thank the Met Office Hadley Centre PRECIS team for their technical and scientific support for the development and application of weather@home. Finally, we would like to thank all of the volunteers who have donated their computing time to climateprediction.net and weather@home.
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