基于多模式集合的中国未来热浪趋势研究

王磊斌; 林齐根; 宋世凯; 刘强; 刘瑞金; 靳少非

doi:10.3878/j.issn.1006-9585.2021.21105

基于多模式集合的中国未来热浪趋势研究

doi: 10.3878/j.issn.1006-9585.2021.21105

王磊斌^{1, 2, 3,},
林齐根⁴,
宋世凯^{1, 2, 3},
刘强^{1, 2, 3},
刘瑞金⁵,
靳少非^6, ,

1.
河北师范大学地理学博士后科研流动站，石家庄 050024
2.
河北师范大学资源与环境科学学院，石家庄 050024
3.
河北省环境变化遥感识别技术创新中心，石家庄 050024
4.
南京信息工程大学地理科学学院，南京 210044
5.
北京城市气象研究院，北京 100089
6.
闽江学院地理与海洋学院，福州 350108

基金项目: 国家重点研发计划项目2018YFC1507801，河北师范大学科研基金资助项目L2021B26、13505275，河北省自然科学基金资助项目D2021205013

详细信息

作者简介:
王磊斌，男，1991年出生，博士，主要从事极端天气气候事件研究。E-mail: leibin.wang@hebtu.edu.cn

通讯作者:
靳少非，E-mail: jinsf@tea.ac.cn

中图分类号: P467
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- 被引次数: 0
出版历程
- 收稿日期: 2021-06-15
- 网络出版日期: 2021-11-12
- 刊出日期: 2022-01-25

Future Heatwave Trends in China Based on Multimodel Ensemble

Leibin WANG^{1, 2, 3
,},
Qigen LIN⁴,
Shikai SONG^{1, 2, 3},
Qiang LIU^{1, 2, 3},
Ruijin LIU⁵,
Shaofei JIN^{6
, ,}

1.
Postdoctoral Research Station of Geography, Hebei Normal University, Shijiazhuang 050024
2.
College of Resources and Environment Science, Hebei Normal University, Shijiazhuang 050024
3.
Hebei Innovation Center for Remote Sensing Identification Technology of Environmental Change, Shijiazhuang 050024
4.
School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044
5.
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
6.
College of Geography and Oceangraphy, Minjiang University, Fuzhou 350108

Funds: National Key Research and Development Program of China (Grant 2018YFC1507801), Science Foundation of Hebei Normal University (Grants L2021B26 and 13505275) , Natural Science Foundation of Hebei Province (Grants D2021205013)

摘要

摘要: 全球变化导致极端天气事件频发，尤其是高温热浪严重影响我国农业生态系统及人类健康。关于热浪事件的定义一直存在着许多争议，对热浪变化趋势空间分布特征的认识有待进一步提高。本文使用气温日较差、绝对温度与相对温度相结合的热浪指标，基于9个CMIP6气候模式的多模式集合结果，评估了可持续发展情景（SSP1-2.6）、中度发展情景（SSP2-4.5）及常规排放情境（SSP5-8.5）下未来中国高温热浪事件的时空分布及变化特征。结果表明：（1）SSP1-2.6情景下未来热浪事件在2050年前后达到顶峰，之后趋于稳定，而在SSP2-4.5情景下，热浪频次、日数及最长持续时间均呈现上升态势，SSP5-8.5情景下热浪的增长趋势及严重程度均为最高；（2）华南、华中地区未来面临更大的热浪风险，SSP5-8.5情景下的热浪频次及强度约是SSP1-2.6的2倍及以上，SSP2-4.5约是SSP1-2.6的1.5倍；（3）西部干旱/半干旱地区、内蒙古东部干旱地区出现较大范围的热浪，结合本文中热浪定义，预示着夜间变暖是全球变暖的一个重要特征。研究结果有助于理解可持续发展、中等强迫情景下我国未来的热浪频次和强度的变化特征，为区域发展节能减排方案的制定提供有效参考。
- 热浪 /
- 热浪趋势 /
- 多模式集合 /
- 未来预估
Abstract: With the intensification of global climate change, extreme weather events will become more frequent, especially heatwaves, seriously affecting agroecosystems and human health. There have been many controversies about the definition of heatwave events, and understanding the spatial distribution characteristics of heatwave trends needs further improvement. Compared with definitions of absolute or relative temperature, this paper adopts a heatwave indicator that considers the daily temperature range and combines both absolute and relative temperature. The spatial distribution and temporal change characteristics of future heatwave events in China were evaluated based on the results of a multimodel ensemble of nine CMIP6 climate models under three different development scenarios: (1) SSP1-2.6, (2) SSP2-4.5, and (3) SSP5-8.5. Results show that (1) future heatwave events under the SSP1-2.6 scenario peaked around 2050 and then stabilized, while the frequency, days, and longest duration of heatwaves under the SSP2-4.5 scenario showed an increasing trend. The growth trend and severity of heatwaves under the SSP5-8.5 scenario are both the highest. (2) South China and Central China will face a greater risk of heatwave occurrence in the future. The frequency and intensity of heatwaves under the SSP5-8.5 scenario are about twice or more than those of SSP1-2.6, while those of SSP2-4.5 are about 1.5 times those of SSP1-2.6. (3) The occurrence of heatwaves of a larger scale in arid/semiarid regions in the west and arid regions in eastern Inner Mongolia, combined with the definition of heatwaves in this paper, predicts that nocturnal warming is an important feature of global warming. Results of the study help to understand the characteristics of future changes in the frequency and intensity of heatwaves in China under sustainable development and medium forcing scenarios and provide effective references for developing energy conservation and emission reduction programs for regional development.
- Heatwave /
- Trend of heatwave /
- Multimodel ensemble /
- Project

HTML全文

图 1 中国主要气候区分布。西部干旱/半干旱区（WAS）、青藏高原地区（QT）、华南地区（S）、华中地区（C）、华北地区（N）、东北地区（NE）、西南地区（SW）和东部干旱区（EA）

Figure 1. Climate subregions across China: Western arid and semiarid region (WAS); Qinghai−Tibet region (QT); southern region (S); center region (C); northern region (N); northeastern region (NE); southwestern region (SW); eastern arid region (EA)

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图 2 1985～2014年（a、d）观测（CN05.1数据）、（b、e）原始多模式集合MME模拟和（c、f）QM 误差订正后的多模式集合MME（QM）模拟的多年平均冬季（第一行）、夏季（第二行）逐日最高气温

Figure 2. Multiyear average daily maximum temperature in winter (first line) and summer (second line) of the observation and simulation during 1985–2014: (a, d) CN05.1 data (observation); (b, e) the multimodel ensemble (MME) result of CMIP6 models; (c, f) MME(QM) is the MME result of CMIP6 models corrected by the QM (quantile mapping) method

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图 3 1985～2014 年（a、c）MME模拟、（b、d）MME（QM）模拟与观测多年平均逐日最高气温的偏差：（a、b）冬季；（c、d）夏季

Figure 3. Deviation of the multiyear average daily maximum temperature among the model simulation of (a, c) MME, (b, d) MME(QM), and the observation during 1985–2014

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图 4 依据CN05.1数据集计算的我国区域热浪事件热浪温度阈值

Figure 4. Threshold temperature of heatwave based on the CN05.1 dataset

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图 5 1985～2014 年（a）观测与（b）MME（QM）模拟的热浪事件频次空间分布

Figure 5. Spatial distribution of the heatwave frequency based on (a) CN05.1 and (b) MME(QM) [multimodel ensemble (quantile mapping)] in the historical period during 1985–2014

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图 6 2071～2100 年（a）SSP1-2.6、（b）SSP2-4.5和（c）SSP5-8.5情景下我国热浪频次空间分布

Figure 6. Spatial distribution of the heatwave frequency in the future during 2071–2100 under (a) SSP1-2.6, (b) SSP2-4.5, and (c) SSP5-8.5 scenarios

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图 7 1985～2100年区域平均热浪频次（所有发生热浪区域热浪频次平均值）时间变化特征（7窗口滑动平均）。虚线为多模式集合历史与未来预估的时间分界线（separate）

Figure 7. Time series characteristics of the regional average heatwave frequency from 1985 to 2100 (seven window sliding average). The dotted line (separate) is the time dividing the line between the historical and future estimates of the multimodal set

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图 8 （a）SSP1-2.6、（b）SSP2-4.5和（c）SSP5-8.5情景下多模式预估中国区域逐日气温异常序列。以CN05.1自1985～2014年为基准时期，tasmax为逐日最高温，tasmin为逐日最低温

Figure 8. Temperature anomaly of the multimodel ensemble in China under (a) SSP1-2.6, (b) SSP2-4.5, and (c) SSP5-8.5 scenarios (using CN05.1 from 1985 to 2014 as baseline). Tasmax indicates the daily maximum temperature and tasmin corresponds to the daily minimum temperature

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图 9 同图7，但为区域平均热浪日数时间变化特征

Figure 9. Same as Fig. 7, but for time series characteristics of regional average heatwave days

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图 10 同图7，但为区域平均最长热浪持续时间变化特征，

Figure 10. Same as Fig. 7, but for time series characteristics of the regional average heatwave duration

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表 1 本文所用CMIP6中9个气候模式概况

Table 1. Overview of nine CMIP6 models used in this study

模式名称	水平网格格点数	历史时段	未来时段	机构/国家
ACCESS-ESM1-5	192 $ \times $ 145	1850～2014年	2015～2100年	CSIRO（Commonwealth Scientific and Industrial Research Organisation）/澳大利亚
BCC-CSM2-MR	320 $ \times $160	1850～2014年	2015～2100年	BCC（Beijing Climate Center）/中国
CanESM5	128 $ \times $ 64	1850～2014年	2015～2100年	CCCma（Canadian Centre for Climate Modelling and Analysis）/加拿大
EC-Earth3	512 $ \times $ 256	1850～2014年	2015～2100年	EC-Earth-Consortium/瑞典
INM-CM5-0	180 $ \times $ 120	1850～2014年	2015～2100年	INM（Institute for Numerical Mathematics）/俄罗斯
IPSL-CM6A-LR	144 $ \times $ 143	1850～2014年	2015～2100年	IPSL（Institut Pierre Simon Laplace）/法国
MIROC6	256 $ \times $ 128	1850～2014年	2015～2100年	MIROC/日本
MPI-ESM1-2-HR	384 $ \times $ 192	1850～2014年	2015～2100年	DKRZ（Deutsches Klimarechenzentrum）/德国
NESM3	192 $ \times $ 96	1850～2014年	2015～2100年	NUIST（Nanjing University of Information Science and Technology）/中国

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表 2 本文所用热浪指标

Table 2. Criteria for the heatwave used in this study

热浪指标	定义
热浪频次（HWF）	每年热浪事件累计发生次数
热浪日数（HWD）	每年热浪事件的累计日数
最长热浪持续时间（LHWD）	每年持续时间最长热浪过程的日数

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