首页|基于多情景模拟试验的全球平均气温长期记忆性归因研究

基于多情景模拟试验的全球平均气温长期记忆性归因研究

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近年来,气候系统中的长期记忆性特征已被广泛应用到气候的多个研究领域,但这一特征的可能来源目前还不清楚.利用去趋势的波动分析方法,本文以全球平均气温为研究对象,分析了多模式在不同情境下的模拟试验结果,对长期记忆性的可能来源进行了初步探究.研究发现,全球平均气温序列具有较强的长期记忆性,且这一特征主要来源于气候系统内部自然变率的贡献.自然外强迫(尤其是火山活动)影响的引入可以显著增强全球平均气温序列的记忆性强度,但人为外强迫具有减弱这一特征的效应.在全球变暖的背景下,受温室气体辐射强迫的影响,全球平均气温序列的长期记忆性特征具有减弱的趋势,这预示着基于记忆性的气候可预报性在未来可能有所降低.为了更好的预测并应对气候变化,本文的结果从气候记忆性的角度强调了减排和温控的重要性.
Understanding long-term memory in global mean temperature: An attribution study based on model simulations
Long-term memory (LTM) in the climate system has been well recognized and applied in different research fields,but the origins of this property are still not clear.In this work,the authors contribute to this issue by studying model simulations under different scenarios.The global mean temperatures from pre-industrial control runs (piControl),historical (all forcings) simulations,natural forcing only simulations (HistoricalNat),greenhouse gas forcing only simulations (HistoricalGHG),etc.,are analyzed using the detrended fluctuation analysis.The authors find that the LTM already exists in the piControl simulations,indicating the important roles of internal natural variability in producing the LTM.By comparing the results among different scenarios,the LTM from the piControl runs is further found to be strengthened by adding natural forcings such as the volcanic forcing and the solar forcing.Accordingly,the observed LTM in the climate system is suggested to be mainly controlled by both the'internal'natural variability and the'external'natural forcings.The anthropogenic forcings,however,may weaken the LTM.In the projections from RCP2.6 to RCP8.5,a weakening trend of the LTM strength is found.In view of the close relations between the climate memory and the climate predictability,a reduced predictability may be expected in a warming climate.

Long-term memorymodel simulationsattributiondetrended fluctuation analysis

QIU Min、YUAN Naiming、YUAN Shujie

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School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu, China

CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

长期记忆性 模式模拟 归因 去趋势的波动分析方法

This work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of China

41675088]the CAS Pioneer Hundred Talents Program

2020

大气和海洋科学快报(英文版)
中国科学院大气物理研究所

大气和海洋科学快报(英文版)

CSCD
影响因子：0.465
ISSN：1674-2834
年,卷(期)：2020.13(5)
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