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<p>The Pacific Decadal Oscillation (PDO) is the leading mode of North Pacific climate variability, yet its response to climate change remains uncertain. Here, we use Linear Inverse Model (LIM) diagnostics to decompose PDO into three dynamical constituents: the Kuroshio-Oyashio Extension (KOE) mode, the North Pacific–Central Tropical Pacific (NP-CP) mode, and the El Niño–Southern Oscillation (ENSO) mode. Applying an observationally derived LIM large ensemble, we show that the relative importance of these modes varies substantially over 85-year periods due to internal climate variability—requiring at least 300 years for stationary estimates. LIMs trained on climate model ensembles reveal that, despite comparable variability, models exhibit systematic biases in representing the spatial structures of the KOE and NP-CP modes. Under global warming, models project a more dominant ENSO contribution and a diminished KOE influence, leading to a shortened PDO timescale. This LIM-based dynamical decomposition enables more direct comparisons of PDO mechanisms between models and observations.</p>

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Decomposition of Pacific Decadal Oscillation using linear inverse models sheds light on its dominant modes and future response

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Sheng Wu,
  • Emanuele Di Lorenzo,
  • Yingying Zhao,
  • Matthew Newman,
  • Zhengyu Liu,
  • Antonietta Capotondi,
  • Daoxun Sun,
  • Samantha Stevenson,
  • Yonggang Liu

摘要

The Pacific Decadal Oscillation (PDO) is the leading mode of North Pacific climate variability, yet its response to climate change remains uncertain. Here, we use Linear Inverse Model (LIM) diagnostics to decompose PDO into three dynamical constituents: the Kuroshio-Oyashio Extension (KOE) mode, the North Pacific–Central Tropical Pacific (NP-CP) mode, and the El Niño–Southern Oscillation (ENSO) mode. Applying an observationally derived LIM large ensemble, we show that the relative importance of these modes varies substantially over 85-year periods due to internal climate variability—requiring at least 300 years for stationary estimates. LIMs trained on climate model ensembles reveal that, despite comparable variability, models exhibit systematic biases in representing the spatial structures of the KOE and NP-CP modes. Under global warming, models project a more dominant ENSO contribution and a diminished KOE influence, leading to a shortened PDO timescale. This LIM-based dynamical decomposition enables more direct comparisons of PDO mechanisms between models and observations.