Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
收藏NIAID Data Ecosystem2026-03-13 收录
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https://zenodo.org/record/5636998
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Abstract from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models':
Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.
《末次千年再分析与CMIP5模式中与年代际全球温度变率相关的局地区域》一文摘要:尽管年代际气候变率具有重要研究价值,但目前我们对在该时间尺度上与全球温度变率相关联的地理区域仍认识有限。器测气候记录往往时长过短,难以构建样本统计量以开展年代际气候变率研究;而第五次耦合模式比较计划(Coupled Model Intercomparison Project Phase 5, CMIP5)的气候模式,在"哪些区域对全球平均年代际温度变率影响最为显著"这一问题上存在分歧。本文采用一款新型古气候数据同化产品——末次千年再分析(Last Millennium Reanalysis, LMR),探究年代际时间尺度下局地变率与全球平均温度变率相关联的区域。LMR框架采用集合卡尔曼滤波(ensemble Kalman filter)数据同化方法,整合最新古气候数据与顶尖数值模式数据,生成逐年分辨率的地表温度场重建结果,使我们能够以全新视角探究前器测时期气候变率的时序特征与动力学机制。LMR的结果一致表明,中高纬度北太平洋与高纬度北大西洋在年代际时间尺度上往往领先于全球温度变率。这一发现对于理解前工业化时期低频气候变率的动力学机制具有重要意义。
创建时间:
2021-11-02



