• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

中国大陆冬季极端温度事件的多模式集合预测。

Multi-Model Ensemble Projections of Winter Extreme Temperature Events on the Chinese Mainland.

机构信息

Hunan Key Laboratory of Remote Sensing of Ecological Environment in Dongting Lake Area, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China.

Key Laboratory of Regional Ecology and Environmental Change, State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China.

出版信息

Int J Environ Res Public Health. 2022 May 12;19(10):5902. doi: 10.3390/ijerph19105902.

DOI:10.3390/ijerph19105902
PMID:35627439
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9141196/
Abstract

Based on the downscaling data of multi-model ensembles of 26 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6, this study calculated the extreme climate indices defined by the Expert Team on Climate Change Detection and Indices and the warm winter extreme grade indices to explore winter climate response on the Chinese mainland under different shared socioeconomic pathways (SSPs) and representative concentration pathways. The results showed that the temperature in winter increased overall, with the highest temperature increases of 0.31 °C/10a (Celsius per decade) (SSP245) and 0.51 °C/10a (SSP585) and the lowest temperature increases of 0.30 °C/10a (SSP245) and 0.49 °C/10a (SSP585). Warm-related extreme weather events such as warm days and warm spell duration indices showed an increasing trend, whereas cold-related extreme weather events such as cold spell duration indices, cold nights, ice days, and frost days showed a decreasing trend. On the regional scale, the maximum temperature increased by more than 2 °C/10a (SSP245) and 0.4 °C/10a (SSP585), except in South China, and the minimum temperature increased faster in Qinghai-Tibet and Northeast China compared to elsewhere on the Chinese mainland. Compared with that under SSP585, the frequency and intensity of warm winters in the latter half of the 21st century were lower under SSP245. At the end of the 21st century, under the SSP245 scenario, warm winter frequency in most regions will be reduced to below 60%, but under the SSP585 scenario, it will be more than 80%. Population exposures all showed a downward trend, mainly due to the reduction of warm winter events and the decline of the population under the SSP245 and SSP585 scenarios, respectively. If the greenhouse gas emission path is controlled in the SSP245 scenario, the population exposure risk in warm winters can be decreased by 25.87%. This study observed a consistent warming trend on the Chinese mainland under all SSPs in the 21st century and found that stricter emission reduction policies can effectively decrease the population exposure to warm winters.

摘要

基于 26 个耦合模式比较计划第六阶段(CMIP6)的多模式集合的降尺度数据,本研究计算了由气候变化检测和指数专家小组以及暖冬极端等级指数定义的极端气候指数,以探索不同共享社会经济路径(SSP)和代表性浓度路径下中国大陆的冬季气候响应。结果表明,冬季气温总体呈上升趋势,升温幅度最高的为 0.31°C/10a(SSP245 和 SSP585),最低的为 0.30°C/10a(SSP245)和 0.49°C/10a(SSP585)。暖相关极端天气事件,如暖日和暖期持续指数呈上升趋势,而冷相关极端天气事件,如冷期持续指数、冷夜、冰日和霜日呈下降趋势。在区域尺度上,除华南地区外,大部分地区的最高气温上升了 2°C/10a(SSP245)和 0.4°C/10a(SSP585),而青藏高原和东北地区的最低气温上升速度比中国大陆其他地区更快。与 SSP585 相比,21 世纪后半叶,SSP245 下暖冬的频率和强度较低。在 21 世纪末,在 SSP245 情景下,大多数地区的暖冬频率将降至 60%以下,但在 SSP585 情景下,将超过 80%。人口暴露度均呈下降趋势,主要是由于暖冬事件的减少以及 SSP245 和 SSP585 情景下人口的减少。如果温室气体排放路径控制在 SSP245 情景下,暖冬人口暴露风险可降低 25.87%。本研究观察到在 21 世纪所有 SSP 下中国大陆的一致变暖趋势,并发现更严格的减排政策可以有效降低人口对暖冬的暴露风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/0c5b00577dd0/ijerph-19-05902-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/b8b99f022f1b/ijerph-19-05902-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/013fbe31b135/ijerph-19-05902-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/1c40d9086b79/ijerph-19-05902-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/ea53487a732a/ijerph-19-05902-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/9a75e845b39a/ijerph-19-05902-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/def64c3221f4/ijerph-19-05902-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/036e3187bf2b/ijerph-19-05902-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/c3cb20ab5650/ijerph-19-05902-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/3d3bc9ce57b2/ijerph-19-05902-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/3737b5c6c4b4/ijerph-19-05902-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/67c8a33a240f/ijerph-19-05902-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/566bb5783794/ijerph-19-05902-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/bd0fa257b2ab/ijerph-19-05902-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/0c5b00577dd0/ijerph-19-05902-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/b8b99f022f1b/ijerph-19-05902-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/013fbe31b135/ijerph-19-05902-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/1c40d9086b79/ijerph-19-05902-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/ea53487a732a/ijerph-19-05902-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/9a75e845b39a/ijerph-19-05902-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/def64c3221f4/ijerph-19-05902-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/036e3187bf2b/ijerph-19-05902-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/c3cb20ab5650/ijerph-19-05902-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/3d3bc9ce57b2/ijerph-19-05902-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/3737b5c6c4b4/ijerph-19-05902-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/67c8a33a240f/ijerph-19-05902-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/566bb5783794/ijerph-19-05902-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/bd0fa257b2ab/ijerph-19-05902-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71ac/9141196/0c5b00577dd0/ijerph-19-05902-g014.jpg

相似文献

1
Multi-Model Ensemble Projections of Winter Extreme Temperature Events on the Chinese Mainland.中国大陆冬季极端温度事件的多模式集合预测。
Int J Environ Res Public Health. 2022 May 12;19(10):5902. doi: 10.3390/ijerph19105902.
2
Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region.量化未来气候极端指数:对西非可持续城市发展的影响,重点关注大阿克拉地区。
Discov Sustain. 2024;5(1):167. doi: 10.1007/s43621-024-00352-w. Epub 2024 Jul 29.
3
Warming and Wetting will continue over the Tibetan Plateau in the Shared Socioeconomic Pathways.在共享社会经济途径下,青藏高原的增暖和增湿仍将持续。
PLoS One. 2023 Aug 4;18(8):e0289589. doi: 10.1371/journal.pone.0289589. eCollection 2023.
4
Spatial and temporal variations of extreme climate index in the Songhua River Basin during 1961-2020.1961—2020年松花江流域极端气候指数的时空变化
Ying Yong Sheng Tai Xue Bao. 2023 Apr;34(4):1091-1101. doi: 10.13287/j.1001-9332.202304.024.
5
Spatiotemporal variability characteristics of extreme climate events in Xinjiang during 1960-2019.1960—2019年新疆极端气候事件的时空变化特征
Environ Sci Pollut Res Int. 2023 Apr;30(20):57316-57330. doi: 10.1007/s11356-023-26514-3. Epub 2023 Mar 24.
6
Future ozone-related acute excess mortality under climate and population change scenarios in China: A modeling study.未来中国气候和人口变化情景下与臭氧有关的急性超额死亡率:一项建模研究。
PLoS Med. 2018 Jul 3;15(7):e1002598. doi: 10.1371/journal.pmed.1002598. eCollection 2018 Jul.
7
[Temporal and spatial variations of extreme climatic events in Songnen Grassland, Northeast China during 1960-2014].1960 - 2014年中国东北松嫩草地极端气候事件的时空变化
Ying Yong Sheng Tai Xue Bao. 2017 Jun 18;28(6):1769-1778. doi: 10.13287/j.1001-9332.201706.002.
8
Extreme precipitation indices over India using CMIP6: a special emphasis on the SSP585 scenario.使用CMIP6对印度极端降水指数的研究:特别关注SSP585情景
Environ Sci Pollut Res Int. 2023 Apr;30(16):47119-47143. doi: 10.1007/s11356-023-25649-7. Epub 2023 Feb 3.
9
Uncertainty in surface wind speed projections over the Iberian Peninsula: CMIP6 GCMs versus a WRF-RCM.伊比利亚半岛地面风速预测的不确定性:CMIP6全球气候模式与WRF区域气候模式的对比
Ann N Y Acad Sci. 2023 Nov;1529(1):101-108. doi: 10.1111/nyas.15063. Epub 2023 Sep 16.
10
How climate change may shift power demand in Japan: Insights from data-driven analysis.气候变化可能如何改变日本的电力需求:来自数据驱动分析的见解。
J Environ Manage. 2023 Nov 1;345:118799. doi: 10.1016/j.jenvman.2023.118799. Epub 2023 Sep 9.

引用本文的文献

1
The impact of non-optimum temperatures, heatwaves and cold spells on out-of-hospital cardiac arrest onset in a changing climate in China: a multi-center, time-stratified, case-crossover study.非适宜温度、热浪和寒潮对中国气候变化背景下院外心脏骤停发病的影响:一项多中心、时间分层的病例交叉研究。
Lancet Reg Health West Pac. 2023 Apr 29;36:100778. doi: 10.1016/j.lanwpc.2023.100778. eCollection 2023 Jul.
2
Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning.利用深度学习从视角对 S2S 业务预报进行准周期信号分析。
Sci Rep. 2023 Mar 13;13(1):4108. doi: 10.1038/s41598-023-31394-1.

本文引用的文献

1
Sources of uncertainty for wheat yield projections under future climate are site-specific.未来气候条件下小麦产量预测的不确定性来源因地点而异。
Nat Food. 2020 Nov;1(11):720-728. doi: 10.1038/s43016-020-00181-w. Epub 2020 Nov 2.
2
Evaluation and Future Projection of Extreme Climate Events in the Yellow River Basin and Yangtze River Basin in China Using Ensembled CMIP5 Models Data.利用集合 CMIP5 模式数据评估和预测中国黄河流域和长江流域的极端气候事件。
Int J Environ Res Public Health. 2021 Jun 3;18(11):6029. doi: 10.3390/ijerph18116029.
3
Well below 2 °C: Mitigation strategies for avoiding dangerous to catastrophic climate changes.
远低于 2°C:避免危险到灾难性气候变化的缓解策略。
Proc Natl Acad Sci U S A. 2017 Sep 26;114(39):10315-10323. doi: 10.1073/pnas.1618481114. Epub 2017 Sep 14.
4
Direct and indirect ENSO modulation of winter temperature over the Asian-Pacific-American region.厄尔尼诺-南方涛动(ENSO)对亚太-美洲地区冬季温度的直接和间接调制
Sci Rep. 2016 Nov 8;6:36356. doi: 10.1038/srep36356.
5
Contribution of changes in atmospheric circulation patterns to extreme temperature trends.大气环流模式变化对极端温度趋势的贡献。
Nature. 2015 Jun 25;522(7557):465-9. doi: 10.1038/nature14550.
6
Warming experiments underpredict plant phenological responses to climate change.变暖实验低估了植物对气候变化的物候反应。
Nature. 2012 May 2;485(7399):494-7. doi: 10.1038/nature11014.
7
Winter and spring warming result in delayed spring phenology on the Tibetan Plateau.冬季和春季变暖导致青藏高原春季物候期推迟。
Proc Natl Acad Sci U S A. 2010 Dec 21;107(51):22151-6. doi: 10.1073/pnas.1012490107. Epub 2010 Nov 29.
8
Shifting plant phenology in response to global change.植物物候对全球变化的响应转移
Trends Ecol Evol. 2007 Jul;22(7):357-65. doi: 10.1016/j.tree.2007.04.003. Epub 2007 May 2.