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<p>On September 16th, 2023, an anomalous 10.88 mHz seismic signal was observed globally, persisting for 9 days. One month later an identical signal appeared, lasting for another week. Several studies have theorized that these signals were produced by seiches which formed after two landslide-generated mega-tsunamis in an East Greenland fjord. This theory is supported by seismic inversions, and analytical and numerical modeling, but no direct observations have been made. Here, we present primary observations of this phenomenon using data from the Surface Water Ocean Topography mission. By ruling out other oceanographic processes, we validate the seiche theory of previous authors and independently estimate its initial amplitude at 7.9 m using Bayesian machine learning and seismic data. This study demonstrates the value of satellite altimetry for studying fast oceanic processes and extreme events, while also highlighting the need for specialized methods to address the altimetric data’s limitations, namely temporal sparsity. These data and approaches will help in understanding future unseen extremes driven by climate change.</p>

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Observations of the seiche that shook the world

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月3日

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  • Thomas Monahan,
  • Tianning Tang,
  • Stephen Roberts,
  • Thomas A. A. Adcock

摘要

On September 16th, 2023, an anomalous 10.88 mHz seismic signal was observed globally, persisting for 9 days. One month later an identical signal appeared, lasting for another week. Several studies have theorized that these signals were produced by seiches which formed after two landslide-generated mega-tsunamis in an East Greenland fjord. This theory is supported by seismic inversions, and analytical and numerical modeling, but no direct observations have been made. Here, we present primary observations of this phenomenon using data from the Surface Water Ocean Topography mission. By ruling out other oceanographic processes, we validate the seiche theory of previous authors and independently estimate its initial amplitude at 7.9 m using Bayesian machine learning and seismic data. This study demonstrates the value of satellite altimetry for studying fast oceanic processes and extreme events, while also highlighting the need for specialized methods to address the altimetric data’s limitations, namely temporal sparsity. These data and approaches will help in understanding future unseen extremes driven by climate change.