极端气候事件的观测、模拟与预测综述-地球科学研究-CSCIED科技核心评价数据库-手机版

极端气候事件的观测、模拟与预测综述

A review of observation, simulation, and prediction of extreme climate events

ES评分 0 浏览量：123 下载量：0

DOI	10.12208/j.jesr.20240003
刊名	地球科学研究 Journal of Earth Science Research
年，卷(期)	2024, 3(1)
作者	程鑫,
作者单位	成都信息工程大学四川成都
摘要	极端气候事件是指发生概率极低但影响巨大的气象异常现象，包括极端降水、干旱、高温、寒潮、风暴等。这些事件不仅对自然生态系统造成严重影响，还对农业生产、水资源管理、城市基础设施及社会经济安全带来显著威胁。随着全球气候变化的加剧，极端事件的频率和强度呈现上升趋势，科学监测、精确模拟及可靠预测已成为应对气候风险的重要手段。近年来，观测技术不断进步，卫星遥感、地面气象站网络及再分析数据提供了高分辨率、长时间序列的数据支持；同时，全球和区域气候模式的发展，使得对极端事件的形成机制、时空演变及未来趋势的研究更加深入。统计学方法、动力学模式及人工智能技术的结合，为极端事件预测提供了新的思路。本文综述了极端气候事件的观测方法、模拟技术及预测手段，分析了不同研究方法的优势与局限，并讨论了极端事件对生态系统、农业、城市环境及社会经济的综合影响。最后，提出了未来研究的关键方向，包括多源数据融合、多模式集合预报及跨学科协同研究，以期为科学决策和风险管理提供理论依据和技术支撑。
Abstract	Extreme weather events refer to meteorological anomalies with extremely low probability but great impact, including extreme precipitation, drought, high temperature, cold wave, storm, etc. These events not only have a serious impact on natural ecosystems, but also pose a significant threat to agricultural production, water resources management, urban infrastructure and social and economic security. With the aggravation of global climate change, the frequency and intensity of extreme events show an upward trend. Scientific monitoring, accurate simulation and reliable prediction have become important means to deal with climate risks. In recent years, observation technology has been continuously improved, satellite remote sensing, ground weather station network and reanalysis data provide high-resolution, long-time series data support; at the same time, the development of global and regional climate models makes the formation mechanism, spatio-temporal evolution and future trends of extreme events more in-depth research. The combination of statistical methods, dynamic models and artificial intelligence technology provides new ideas for extreme event prediction. This paper summarizes the observation methods, simulation techniques and prediction methods of extreme weather events, analyzes the advantages and limitations of different research methods, and discusses the comprehensive effects of extreme weather events on ecosystems, agriculture, urban environment and social economy. Finally, the key directions of future research, including multi-source data fusion, multi-model ensemble forecasting and interdisciplinary collaborative research, are proposed to provide theoretical basis and technical support for scientific decision-making and risk management.
关键词	极端气候事件；观测；模拟；预测；气候变化
KeyWord	Extreme weather events; Observation; Simulation; Prediction; Climate change
基金项目
页码	14-24

参考文献
相关文献

[1] Samantray P ,Gouda C K . A review on the extreme rainfall studies in India
[J].Natural Hazards Research,2024, 4(3): 347-356.

[2] 黄丽华,何云玲,阮文洁. 基于CMIP6气候模式的西南地区极端气候指数评估与预估
[J].云南大学学报(自然科学版),2024,46(04):686-696.

[3] 苏韦韬.基于土壤湿度的极端干湿事件演变和CMIP6模式评估
[D].南京信息工程大学,2024.

[4] 刘柯莹.气候变化条件下嘉陵江流域极端气候事件时空演变特征研究
[D].华北电力大学(北京),2024.

[5] 刘彩红,极端气候事件对青海湖流域水资源影响的定量评估模型.青海省,青海省气候中心,2024-01-08.

[6] 施宇.极端天气事件对粮食生产的影响及其缓解途径
[D].西北农林科技大学,2023.

[7] 魏宇晨.亚洲中高纬植被对极端气候的响应及其模拟评估
[D].南京信息工程大学,2023.

[8] 毛紫怡.气候系统模式FGOALS-g3对东亚冬季风的模拟和预估
[D].云南大学,2023.

[9] 严一霖.中国极端天气事件的时空变化及未来趋势预估
[D].成都信息工程大学,2023.

[10] 王燕子.气候变化与极端事件对冬油菜物候和产量的影响
[D].西北农林科技大学,2023.

[11] 王雅青.城市环境中香樟光合特性对极端高温干旱的响应
[D].中南林业科技大学,2023.

[12] 牟莎.基于CMIP6的中国复合极端事件时空变化及未来预估
[D].华中师范大学,2023.

[13] Y. S P ,F. S K ,Jan G O V , et al. Rapid attribution analysis of the extraordinary heat wave on the Pacific coast of the US and Canada in June 2021
[J].Earth System Dynamics, 2022,13(4):1689-1713.

[14] 荐圣淇,毛峙闻,温跃修,等. 黄河流域极端气候变化气候模式优选
[J].人民黄河,2022,44(09):83-88.

[15] 雷景春.基于WRF模式的渭河流域极端降水模拟研究
[D].西安理工大学,2022.

[16] 徐浩然.基于CWRF对我国东部极端气候变化及青藏高原加热作用的研究
[D].南京信息工程大学,2022.

[17] 袁旻舒.陆地生态系统总初级生产力对极端气候的响应
[D].西北农林科技大学,2022.

[18] 朱丹瑶.小兴安岭地表物候特征及其对极端气候的响应
[D].哈尔滨师范大学,2021.

[19] 陈雅文.水位变化对黄河三角洲湿地生态系统CO2交换影响的模拟研究
[D].中国科学院大学(中国科学院烟台海岸带研究所),2021.

[20] 陆云.对流允许尺度下中国东部未来极端降水变化的预估
[D].南京大学,2021.

[21] 李彦萌,刘海鹏,张冬峰,等. 华北地区极端气候事件对农作物生长的影响研究
[J].种子科技,2021,39(05):3-8.

[22] 陈虹举,杨建平,丁永建,等. 多模式产品对青藏高原极端气候模拟能力评估
[J].高原气象,2021,40(05):977-990.

[23] Sajjad H ,Ghaffar A . Observed, simulated and projected extreme climate indices over Pakistan in changing climate
[J]. Theoretical and Applied Climatology,2019, 137(1-2): 255-281.

[24] Oleg A ,Robert O . Climate change in Northern Russia through the prism of public perception.
[J]. Ambio,2019, 48(6):661-671.

[25] Anisimov O ,Orttung R . Climate change in Northern Russia through the prism of public perception
[J]. Ambio, 2018,1-11.

引用本文

程鑫. 极端气候事件的观测、模拟与预测综述 [J]. 地球科学研究. 2024; 3; (1). 14 - 24.

文献评论

相关学者

相关机构