王安乾,苏布达,王艳君,黄金龙,温姗姗,姜彤. 2017. 全球升温1.5℃与2.0℃情景下中国极端低温事件变化与耕地暴露度研究[J]. 气象学报, 75(3):415-428, doi:10.11676/qxxb2017.029
全球升温1.5℃与2.0℃情景下中国极端低温事件变化与耕地暴露度研究
Variation of the extreme low-temperature events and farmland exposure under global warming of 1.5℃ and 2.0℃
投稿时间:2016-12-26  修订日期:2017-03-01
DOI:10.11676/qxxb2017.029
中文关键词:  全球升温1.5℃和2.0℃  极端低温事件  耕地暴露度  强度-面积-持续时间  CCLM模式
英文关键词:Global warming of 1.5℃ and 2.0℃  Extreme low-temperature events  Exposure of farmland  Intensity-area-duration  CCLM
基金项目:国家自然科学基金项目(41671211、41401056)、国家自然科学基金会和巴基斯坦科学基金会合作研究项目(41661144027)、新疆维吾尔自治区高层次引进人才项目(Y642091、Y644131)。
作者单位E-mail
王安乾 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐, 830011
中国科学院大学, 北京, 100049 
 
苏布达 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐, 830011
南京信息工程大学气象灾害预报预警与评估协同中心/地理与遥感学院, 南京, 210044
中国气象局国家气候中心, 北京, 100081 
 
王艳君 南京信息工程大学气象灾害预报预警与评估协同中心/地理与遥感学院, 南京, 210044  
黄金龙 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐, 830011
中国科学院大学, 北京, 100049 
 
温姗姗 中国科学院新疆生态与地理研究所, 荒漠与绿洲生态国家重点实验室, 乌鲁木齐, 830011
中国科学院大学, 北京, 100049 
 
姜彤 南京信息工程大学气象灾害预报预警与评估协同中心/地理与遥感学院, 南京, 210044
中国气象局国家气候中心, 北京, 100081 
jiangtong@cma.gov.cn 
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中文摘要:
      基于区域气候模式COSMO-CLM(CCLM)模拟的1960-2100年逐日最低气温数据及2000年中国土地利用数据,采用强度-面积-持续时间(Intensity-Area-Duration,IAD)方法,以全球升温1.5℃(RCP 2.6情景)和2.0℃(RCP 4.5情景)为目标,研究不同持续时间中国极端低温事件变化特征、最强极端低温事件强度与面积关系和最强中心空间分布,分析极端低温事件下耕地面积暴露度的变化规律。研究发现:(1)全球升温1.5℃情景下,持续1至9 d的极端低温事件频次相对于基准期(1986-2005年)下降30%-54%,强度变化-1%-8.8%,影响面积下降7%-21%;升温2.0℃,频次下降48%-80%,强度上升6%-11.5%,影响面积则在-14%-19%变化。(2)全球不同升温情景有可能发生强度和面积超过基准期最强事件的极端低温。全球升温1.5-2.0℃时,同等面积上的最强极端低温事件强度明显下降,但最强极端低温事件中心由西北和西南转移到华中和华南等地。(3)不同升温情景下,暴露于极端低温事件的中国耕地面积明显少于基准期,且升温幅度越高下降程度越大。最强极端低温事件的耕地暴露度则随温度的升高而增大。升温1.5℃时,华东、华北与华中等地暴露在最强极端低温事件的耕地面积相对于基准期有所增大,升温2.0℃时,华东与华北等地有大幅度上升。全球不同升温情景下,极端低温事件频次与影响面积持续下降,但强度上升;随着升温幅度的增大,这种差异变化特征越来越明显;特别应注意的是,随着温度上升,发生强度和面积超过当前记录到的最强极端低温事件的可能性增大;应加强极端事件的预警、预报和监测,减缓经济社会的损失。
英文摘要:
      Based on daily minimum temperatures simulated by the COSMO-CLM (CCLM) for 1960-2100 and the landuse data in 2000 in China, the Intensity-Area-Duration (IAD) method was applied to characterize the extreme low-temperature events, the relationship between the intensity and spatial coverage of extreme low-temperature events, the spatial distribution of the most severe low-temperature events, and the exposure of farmlands to extreme low-temperature events under the global warming of 1.5℃ (RCP 2.6 scenario) and 2.0℃ (RCP 4.5 scenario). The results are as follows. (1) During the period of 1.5℃ warming, the frequency of extreme low-temperature events that can last for one to nine days will decrease by 30%-54%, the intensity will change by -1%-8.8%, and the area influenced by the extreme low-temperature events will decrease by 7%-21% compared to that during the reference period (1986-2005). For the 2.0℃ warming period, the frequency of the extreme low-temperature events will decrease by 48%-80%, the intensity will increase by 6%-11.5%, and the area affected by extreme low-temperature events will change by -14%-19%. (2) There are possibilities that both the intensity of and the area affected by the extreme low-temperature events in the future will exceed that of the most severe event in the reference period. Intensity of the most severe event in the context of 1.5℃ warming will be stronger than that in the context of 2.0℃ warming over the same area, but the center of the most severe event might move from Northwest and Southwest China to Central and South China. (3) Farmland exposure to the extreme low-temperature events will decrease in the warming periods than in the reference period, and the higher the temperature increase, the greater the decrease. However, farmland exposure to the most severe events will increase to a certain extent in East, North and Central China in the context of 1.5℃ warming, and such increases are even more obvious in East and North China in the context of 2.0℃ warming. Aforementioned findings indicate that frequency and coverage of the extreme low-temperature events will decrease, but their intensity might increase with the rising of temperature. With the increasing occurrence probability of the most severe event with higher intensity over larger area than that in the past, improvement of early warning is still imperative for mitigation of the societal and economic impact of extreme events.
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