Reconciling Roles of External Forcing and Internal Variability in Arctic Sea Ice Change on Different Timescales

IF 4.8 2区 地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Climate Pub Date : 2024-03-26 DOI:10.1175/jcli-d-23-0280.1
Zili Shen, Anmin Duan, Wen Zhou, Yuzhuo Peng, Jinxiao Li
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Abstract

Abstract Two large ensemble simulations are adopted to investigate the relative contribution of external forcing and internal variability to Arctic sea ice variability on different timescales since 1960 by correcting the response error of models to external forcing using observational datasets. Our study suggests that previous approaches might overestimate the real impact of internal variability on Arctic sea ice change especially on long time scales. Our results indicate that in both March and September, internal variability plays a dominant role on all time scales over the 20th century, while the anthropogenic signal on sea ice change can be steadily and consistently detected on a time scale of more than 20 year after 2000s. We also reveal that the dominant mode of internal variability in March shows consistency across different time scales. On the contrary, the pattern of internal variability in September is highly nonuniform over the Arctic and varies across different timescales, indicating that sea ice internal variability in September at different time scales is driven by different factors.
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调和不同时间尺度上北极海冰变化中外部强迫和内部变异的作用
摘要 通过利用观测数据集修正模式对外部作用力的响应误差,采用两个大型集合模拟来研究自 1960 年以来不同时间尺度上外部作用力和内部变化对北极海冰变化的相对贡献。我们的研究表明,以往的方法可能会高估内部变率对北极海冰变化的实际影响,尤其是在长时间尺度上。我们的研究结果表明,在 20 世纪的所有时间尺度上,内部变率在 3 月和 9 月都起着主导作用,而人为因素对海冰变化的影响在 2000 年代之后的 20 多年时间尺度上都能稳定、持续地探测到。我们还发现,3 月份内部变率的主导模式在不同时间尺度上表现出一致性。相反,9 月的内部变化模式在北极地区高度不均匀,在不同时间尺度上也各不相同,这表明不同时间尺度上 9 月的海冰内部变化是由不同因素驱动的。
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来源期刊
Journal of Climate
Journal of Climate 地学-气象与大气科学
CiteScore
9.30
自引率
14.30%
发文量
490
审稿时长
7.5 months
期刊介绍: The Journal of Climate (JCLI) (ISSN: 0894-8755; eISSN: 1520-0442) publishes research that advances basic understanding of the dynamics and physics of the climate system on large spatial scales, including variability of the atmosphere, oceans, land surface, and cryosphere; past, present, and projected future changes in the climate system; and climate simulation and prediction.
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