气候变化研究进展 ›› 2025, Vol. 21 ›› Issue (2): 153-168.doi: 10.12006/j.issn.1673-1719.2024.280
中国气候变化检测归因研究进展
孙颖1(
), 王东阡1, 张学斌2
- 1 气候系统预测与变化应对全国重点实验室，国家气候中心，北京 100081
2 维多利亚大学太平洋气候影响联盟，不列颠哥伦比亚省 维多利亚 V8P 5C2，加拿大
-
收稿日期:2024-11-04修回日期:2024-12-18出版日期:2025-03-30发布日期:2025-02-28 -
作者简介:孙颖，女，研究员，sunying@cma.gov.cn -
基金资助:国家自然科学基金(42025503);国家自然科学基金(U2342228);中国气象局重点创新团队“气候变化检测与应对”(CMA2022ZD03)
Progress in climate change detection and attribution studies in China
SUN Ying1(), WANG Dong-Qian1, ZHANG Xue-Bin2
- 1 State Key Laboratory of Climate System Prediction and Risk Management, National Climate Centre, Beijing 100081, China
2 Pacific Climate Impacts Consortium, University of Victoria, Victoria V8P 5C2, British Columbia, Canada
-
Received:2024-11-04Revised:2024-12-18Online:2025-03-30Published:2025-02-28
摘要：
作为气候变化研究中的重大前沿科学领域，气候变化检测归因旨在揭示气候变化的原因，量化外强迫对气候变化的影响程度。这些问题不仅是气候变化科学研究的核心问题，也是气候变化国际谈判的热点和焦点问题。我国在检测归因领域总体起步较晚，但是近十年来，在中国科学家的努力下，我们从无到有实现了对中国区域气候变化和极端事件归因认识的若干突破，在中国气候变化检测归因领域取得了显著的研究进展。文中对该领域主要研究进展的回顾和梳理表明：20世纪中期以来，以温室气体排放为主的人类活动是中国区域变暖，极端温度频率、强度和持续时间变化的主要驱动因子。人类活动对极端降水变化产生了清晰的影响，同时也可在某些类型干旱的变化中发现人类活动的信号。百年时间尺度上，人类活动的信号可以在平均和极端温度指标的变化中检测到。对于重大高影响极端事件，人为强迫增加了极端高温事件发生的概率，减少了极端低温事件发生的概率。人类活动对强降水事件、干旱和复合事件的归因研究结论一致性较低，同时受到了事件定义和归因方法等的影响，要评估得出人类活动对这类事件影响程度的一般性结论仍然非常困难。未来需要加强对降水、干旱、大气环流、复合事件等变化的检测归因，理解并提高极端事件归因结果的可靠性。
引用本文
孙颖, 王东阡, 张学斌. 中国气候变化检测归因研究进展[J]. 气候变化研究进展, 2025, 21(2): 153-168.
SUN Ying, WANG Dong-Qian, ZHANG Xue-Bin. Progress in climate change detection and attribution studies in China[J]. Climate Change Research, 2025, 21(2): 153-168.
图1 基于概率的事件归因 注：图中红色和蓝色分别表示有和没有人类活动影响下气候变量的概率密度分布（实线）和事件的发生概率（P0和 P1，阴影)，红色虚线表示对未来可能的气候变量概率密度的预估，本图根据文献[23]重绘。
Fig. 1 An illustration of the Probability Density Functions (PDFs)
图1 基于概率的事件归因 注：图中红色和蓝色分别表示有和没有人类活动影响下气候变量的概率密度分布（实线）和事件的发生概率（P0和 P1，阴影)，红色虚线表示对未来可能的气候变量概率密度的预估，本图根据文献[23]重绘。
Fig. 1 An illustration of the Probability Density Functions (PDFs)
图2 基于百年均一化观测和CMIP6模式资料的中国平均气温变化的检测归因[32] (a) 1901—2018年，(b) 1951—2018年 注：图中显示为观测到的中国年平均气温变化趋势的最优估计（OBS）以及不同因子贡献的最优估计值，包括全强迫（ALL)、温室气体（GHG)、人为气溶胶（AA）和自然强迫（NAT)。图中的误差栏表示5%~95%的置信区间。
Fig. 2 Detection and attribution of mean temperature changes in China based on station observations and CMIP6 model data. The figure shows attributable changes from ALL, GHG, AA, and NAT forcings to observational trend (OBS) in annual, summer (JJA), and winter (DJF) temperature for the 1901-2018 (a) and 1951-2018 (b) periods[32]
图2 基于百年均一化观测和CMIP6模式资料的中国平均气温变化的检测归因[32] (a) 1901—2018年，(b) 1951—2018年 注：图中显示为观测到的中国年平均气温变化趋势的最优估计（OBS）以及不同因子贡献的最优估计值，包括全强迫（ALL)、温室气体（GHG)、人为气溶胶（AA）和自然强迫（NAT)。图中的误差栏表示5%~95%的置信区间。
Fig. 2 Detection and attribution of mean temperature changes in China based on station observations and CMIP6 model data. The figure shows attributable changes from ALL, GHG, AA, and NAT forcings to observational trend (OBS) in annual, summer (JJA), and winter (DJF) temperature for the 1901-2018 (a) and 1951-2018 (b) periods[32]
图3 基于中国东部（105°E以东）百年逐日观测和CMIP6模式资料的极端气温指标变化检测归因[44](a)1901—2020年，(b) 1951—2020年 注：图中显示为观测到的极端温度变化趋势的最优估计（OBS）以及不同因子贡献的最优估计值，包括全强迫（ALL)、温室气体（GHG)、人为气溶胶（AA）和自然强迫（NAT)，误差栏表示5%~95%的置信区间。
Fig. 3 Detection and attribution of changes in extreme temperature indices based on centennial observations and CMIP6 model data in eastern China. (The figure shows attributable trends to ALL, GHG, AA, and NAT signals compared with the observed trends (OBS) for annual and seasonal indices during 1901-2020 (a) and 1951-2020 (b))[44]
图3 基于中国东部（105°E以东）百年逐日观测和CMIP6模式资料的极端气温指标变化检测归因[44](a)1901—2020年，(b) 1951—2020年 注：图中显示为观测到的极端温度变化趋势的最优估计（OBS）以及不同因子贡献的最优估计值，包括全强迫（ALL)、温室气体（GHG)、人为气溶胶（AA）和自然强迫（NAT)，误差栏表示5%~95%的置信区间。
Fig. 3 Detection and attribution of changes in extreme temperature indices based on centennial observations and CMIP6 model data in eastern China. (The figure shows attributable trends to ALL, GHG, AA, and NAT signals compared with the observed trends (OBS) for annual and seasonal indices during 1901-2020 (a) and 1951-2020 (b))[44]
图4 中国区域Rx1day和Rx5day单信号ALL的比例因子及其5%~95%置信区间的范围及最佳估计值[48] 注：（a、c）图为直接利用极端降水距平进行区域平均计算后得到的比例因子，（b、d）图为将极端降水进行概率拟合转换后再进行平均计算后得到的比例因子。（a、b）图为基于站点数据的结果，（c、d）图为基于格点降水数据集的结果。三角形表示模式模拟的变率太高。
Fig. 4 The best estimates of scaling factors and their 5%-95% confidence intervals of the ALL signal in single-signal analyses for China under different configurations of the analyses[48]
图4 中国区域Rx1day和Rx5day单信号ALL的比例因子及其5%~95%置信区间的范围及最佳估计值[48] 注：（a、c）图为直接利用极端降水距平进行区域平均计算后得到的比例因子，（b、d）图为将极端降水进行概率拟合转换后再进行平均计算后得到的比例因子。（a、b）图为基于站点数据的结果，（c、d）图为基于格点降水数据集的结果。三角形表示模式模拟的变率太高。
Fig. 4 The best estimates of scaling factors and their 5%-95% confidence intervals of the ALL signal in single-signal analyses for China under different configurations of the analyses[48]
图5 中国夏季平均湿球温度全强迫（ALL）单信号和人为强迫（ANT）、自然强迫（NAT）双信号比例因子最佳估计及其25%~75%（箱型图）和5%~95%（横线）置信区间范围(a)，中国西部地区(b)和东部地区(c)在人为辐射强迫（ANT，橙色）和自然强迫（NAT，蓝色）下1961—2010年夏季平均湿球温度概率密度分布[57]
Fig. 5 Climatic conditions that are increasingly conducive to summer heat stress as measured by summer mean wet bulb globe temperature (WGBT) in China have human-induced origins: estimates of scaling factors for one-signal (ALL) and two-signal (ANT and NAT) ?ngerprint analyses (a). The white lines mark the scaling-factor best estimates. The width of the boxplot represents the 25%-75% un-certainty ranges of the scaling-factor estimates, and the whiskers extend to the 5%-95% uncertainties ranges. Also shown are trend histograms of the observation-constrained 1961-2010 summer mean WBGT in a climate with anthropogenic-only forcings (orange) and with natural-only forcings (blue) for western (b) and eastern (c) China. The observed trends are marked by vertical red lines[57]
图5 中国夏季平均湿球温度全强迫（ALL）单信号和人为强迫（ANT）、自然强迫（NAT）双信号比例因子最佳估计及其25%~75%（箱型图）和5%~95%（横线）置信区间范围(a)，中国西部地区(b)和东部地区(c)在人为辐射强迫（ANT，橙色）和自然强迫（NAT，蓝色）下1961—2010年夏季平均湿球温度概率密度分布[57]
Fig. 5 Climatic conditions that are increasingly conducive to summer heat stress as measured by summer mean wet bulb globe temperature (WGBT) in China have human-induced origins: estimates of scaling factors for one-signal (ALL) and two-signal (ANT and NAT) ?ngerprint analyses (a). The white lines mark the scaling-factor best estimates. The width of the boxplot represents the 25%-75% un-certainty ranges of the scaling-factor estimates, and the whiskers extend to the 5%-95% uncertainties ranges. Also shown are trend histograms of the observation-constrained 1961-2010 summer mean WBGT in a climate with anthropogenic-only forcings (orange) and with natural-only forcings (blue) for western (b) and eastern (c) China. The observed trends are marked by vertical red lines[57]
图6 人为辐射强迫对2021年9月中国北方创纪录降水偏多事件的影响[82] （a~e）月平均降水距平百分率（MPPA）的概率密度分布，重现期分布和风险比及其bootstrap检验结果，ALL（红色)、NAT（蓝色)、GHG（紫色)、AA（橙色）和控制试验（CTL，绿色）情景结果使用颜色区分；（f~j）为最大日降水距平百分率（Rx1day%）结果；（k~o）为最大连续5 d降水距平百分率（Rx5day%）结果
Fig. 6 Fitted distributions, return periods, risk ratios, and exceedance probabilities of domain-averaged MPPA (a-e) by the empirical probability formula and Rx1day% (f-j) and Rx5day% (k-o) by a GEV distribution in September 2021 over northern China based on ALL (red), NAT (blue), GHG (purple), AA (orange), and CTL (green) ensembles. Black lines indicate the observed threshold values of the September 2021 event, i.e., 140.5%, 83.87%, and 88.82% for MPPA, Rx1day%, and Rx5day%, respectively. Panels (e), (j), and (o) are best estimates and 90% confidence intervals of risk ratios (left, gray boxes) and exceedance probabilities (right, color bars). The error bars and boxes mark 5%-95% uncertainty ranges estimated via the bootstrapping method (N=1000)[82]
图6 人为辐射强迫对2021年9月中国北方创纪录降水偏多事件的影响[82] （a~e）月平均降水距平百分率（MPPA）的概率密度分布，重现期分布和风险比及其bootstrap检验结果，ALL（红色)、NAT（蓝色)、GHG（紫色)、AA（橙色）和控制试验（CTL，绿色）情景结果使用颜色区分；（f~j）为最大日降水距平百分率（Rx1day%）结果；（k~o）为最大连续5 d降水距平百分率（Rx5day%）结果
Fig. 6 Fitted distributions, return periods, risk ratios, and exceedance probabilities of domain-averaged MPPA (a-e) by the empirical probability formula and Rx1day% (f-j) and Rx5day% (k-o) by a GEV distribution in September 2021 over northern China based on ALL (red), NAT (blue), GHG (purple), AA (orange), and CTL (green) ensembles. Black lines indicate the observed threshold values of the September 2021 event, i.e., 140.5%, 83.87%, and 88.82% for MPPA, Rx1day%, and Rx5day%, respectively. Panels (e), (j), and (o) are best estimates and 90% confidence intervals of risk ratios (left, gray boxes) and exceedance probabilities (right, color bars). The error bars and boxes mark 5%-95% uncertainty ranges estimated via the bootstrapping method (N=1000)[82]
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