Does an intrinsic source generate a shared low-frequency signature in Earth’s climate and rotation rate?

EARTH INTERACTIONS(2016)

引用 4|浏览0
暂无评分
摘要
Previous studies have shown strong negative correlation between multidecadal signatures in length of day (LOD)-an inverse measure of Earth's rotational rate-and various climate indices. Mechanisms remain elusive. Climate processes are insufficient to explain observed rotational variability, leading many to hypothesize external (astronomical) forcing as a common source for observed low-frequency signatures. Here, an internal source, a core-to-climate, one-way chain of causality, is hypothesized. To test hypothesis feasibility, a recently published, model-estimated forced component is removed from an observed dataset of Northern Hemisphere (NH) surface temperatures to isolate the intrinsic component of climate variability, enhancing its comparison with LOD. To further explore the rotational connection to climate indices, the LOD anomaly record is compared with sea surface temperatures (SSTs)-global and regional. Because climate variability is most intensely expressed in the North Atlantic sector, LOD is compared to the dominant oceanic pattern there-the Atlantic multidecadal oscillation (AMO). Results reveal that the LOD-related signal is more global than regional, being greater in the global SST record than in the AMO or in global-mean (land + ocean) or land-only surface temperatures. Furthermore, the strong (4 sigma) correlation of LOD with the estimated NH intrinsic component is consistent with the view proffered here, one of an internally generated, core-to-climate process imprinted on both the climate and Earth's rotational rate. While the exact mechanism is not elucidated by this study's results, reported correlations of geomagnetic and volcanic activity with LOD offer prospects to explain observations in the context of a core-to-climate chain of causality.
更多
查看译文
关键词
Physical meteorology and climatology,Climate change,Temperature,Mathematical and statistical techniques,Time series,Variability,Multidecadal variability
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要