Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models
Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models
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DOI：
10.1029/2019jd030426
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
L. Parsons;G. Hakim
中科院分区：
文献类型：
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作者：
L. Parsons;G. Hakim
尽管年代际气候变率很重要，但我们对这些时间尺度上哪些地理区域与全球温度变率相关的了解有限。仪器记录往往太短，无法发展样本统计来研究年代际气候变率，而耦合模式比对项目第5阶段（CMIP5）气候模式在哪些地点对全球平均年代际温度变率影响最大方面往往存在分歧。在这里，我们使用一种新的古气候资料同化产品——最后千年再分析（Last Millennium Reanalysis， LMR）来研究在年代际时间尺度上，局部变率与全球平均温度变率之间的关系。LMR框架使用集合卡尔曼滤波数据同化方法，将最新的古气候数据和最先进的模式数据结合起来，生成每年分辨率的地表温度场重建，这使我们能够以新的方式探索仪器前气候变率的时间和动态。LMR一致表明，在年代际时间尺度上，中高纬北太平洋和高纬北大西洋倾向于引领全球温度变化。这些发现对于理解工业化前时代低频气候变率的动态具有重要意义。
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.