科技导报 >

2024 , Vol. 42 >Issue 19: 73 - 84

DOI: https://doi.org/10.3981/j.issn.1000-7857.2024.02.00236

专题：气候变化与绿色能源低碳发展

全球碳中和背景下中国气候与极端气候变化

  • 蔡子怡 ,
  • 游庆龙 ,
  • 吴芳营 ,
  • 江志红 ,
  • 翟盘茂
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  • 1. 复旦大学大气与海洋科学系/大气科学研究院, 上海 200438;
    2. 极地海-冰-气系统与天气气候教育部重点实验室, 上海 200438;
    3. 南京信息工程大学气象灾害教育部重点实验室, 南京 210044;
    4. 中国气象科学研究院, 北京 100081
蔡子怡,博士研究生,研究方向为气候变化诊断,电子信箱:zycai19@fudan.edu.cn;游庆龙(通信作者),教授,研究方向为气候与极端气候变化,电子信箱:qlyou@fudan.edu.cn

收稿日期: 2023-12-15

  修回日期: 2024-05-31

  网络出版日期: 2024-11-02

基金资助

国家重点研发计划项目(2023YFE0123800);上海市基础研究特区计划项目(22TQ007)

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China's climate and extreme climate changes under the global carbon neutrality scenario

  • CAI Ziyi ,
  • YOU Qinglong ,
  • WU Fangying ,
  • JIANG Zhihong ,
  • ZHAI Panmao
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  • 1. Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China;
    2. Key Laboratory of Polar Atmosphere-ocean-ice System for Weather and Climate, Ministry of Education, Fudan University, Shanghai 200438, China;
    3. Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing 210044, China;
    4. Chinese Academy of Meteorological Sciences, Beijing 100081, China

Received date: 2023-12-15

  Revised date: 2024-05-31

  Online published: 2024-11-02

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摘要

利用第6次国际耦合模式比较计划（CMIP6）26个模式的3种共享社会经济路径（SSP）结果，选取了SSP1-2.6情景下全球大气CO2浓度达峰时间以确定全球碳中和时间，预估了未来全球碳中和时期相比于历史参考时期（1995—2014年）的中国气候与极端气候的响应变化，并与未实现碳中和的情景结果进行比较。结果表明，SSP1-2.6情景下全球达到碳中和时间为2062年左右（与中国的碳中和实现目标时间接近），相较于历史参考时期，SSP1-2.6碳中和时期中国区域平均升温（1.61±0.46）℃，降水增加（9.15±5.46）%，最大升温和增湿区域位于中国西北，增暖和增湿幅度分别达到（1.84±0.50）℃和（10.05±8.61）%；中国平均白天最高气温和夜间最低气温分别增加（1.78±0.76）℃和（1.83±0.69）℃，白天极端高温在青藏高原存在最大增幅（17.05±5.16）%，夜间极端低温在中国南方下降最为明显（-6.08±0.73）%；极端降水事件整体呈增加趋势，极端强降水在青藏高原最大增幅超过20%，最大连续干旱日数在中国北方减少而在南方增加。相比于未碳中和情景SSP2-4.5和SSP5-8.5，碳中和目标的实现可减缓未来中国的气候变化，极大防控中国大部分区域极端暖事件和极端湿事件的加剧，以及未来中国南方连续干旱日数的增加。因此，为缓解未来中国区域气候变化的加剧，需要合理控制CO2排放以实现“双碳”目标。

关键词： 碳中和; 气候变化; 极端气候变化

本文引用格式

蔡子怡 , 游庆龙 , 吴芳营 , 江志红 , 翟盘茂 . 全球碳中和背景下中国气候与极端气候变化[J]. 科技导报, 2024 , 42(19) : 73 -84 . DOI: 10.3981/j.issn.1000-7857.2024.02.00236

Abstract

This study utilizes the data from 26 CMIP6 models to explore the timeline for global carbon neutrality under the SSP1-2.6 scenario, focusing on the peak CO2 concentration time. It assesses changes in China's climate and extreme climate during the carbon neutrality period, using 1995—2014 as a reference. Meanwhile, these findings are contrasted with the outcomes from scenarios where carbon neutrality was not achieved. The results indicate that under the SSP1-2.6 scenario, global carbon neutrality will be achieved around 2062 (close to China's carbon neutrality target time). The regional average temperature in China during the SSP1-2.6 carbon neutrality period is expected to increase by (1.61±0.46)℃, with a precipitation increase of (9.15±5.46)%. The most significant change areas will be located in northwestern China, with temperature and precipitation increases reaching (1.84±0.50)℃ and (10.05±8.61)%, respectively. The average hottest days and coldest nights in China will increase by (1.78±0.76)℃ and (1.83±0.69)℃, respectively. Warm days will likely increase most significantly on the Tibetan Plateau (17.05±5.16)%, while cool nights decrease most in southern China (-6.08±0.73)%. Extreme precipitation events will intensify, with very wet days near the Tibetan Plateau increasing by more than 20%. Meanwhile, the consecutive dry days will decrease in northern China but increase in the southern regions. Compared to non-carbon neutrality scenarios like SSP2-4.5 and SSP5-8.5, the achievement of dual carbon goals can help mitigate future extreme climate change in China. It helps control extreme temperature and precipitation increases in northern China and the Tibetan Plateau, and reduce consecutive dry days in southern China. Therefore, to alleviate the exacerbation of regional climate change in China in the future, it is crucial to control CO2 emissions more rationally to achieve "dual carbon" goals.

Key words： carbon neutrality; climate change; extreme climate change

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