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Decomposition of pacific decadal oscillation sheds light on its dominant modes and future response using linear inverse model
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  • Published: 10 January 2026

Decomposition of pacific decadal oscillation sheds light on its dominant modes and future response using linear inverse model

  • Sheng Wu1,2,
  • Emanuele Di Lorenzo1,
  • Yingying Zhao3,
  • Matthew Newman4,
  • Zhengyu Liu5,
  • Antonietta Capotondi4,
  • Daoxun Sun3,
  • Samantha Stevenson6 &
  • …
  • Yonggang Liu2,7 

npj Climate and Atmospheric Science , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Climate change
  • Ocean sciences
  • Physical oceanography

Abstract

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.

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Data availability

Data related to the paper can be downloaded from the following websites: HadISSTv1.1,https://www.metoffice.gov.uk/hadobs/hadisst/; NCEP/NCAR Reanalysis, https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.html;CESM large ensemble, https://www.cesm.ucar.edu/community-projects/lens; CMIP6 database, https://esgf-node.llnl.gov/projects/cmip6/.

Code availability

All codes are available from the corresponding author on request.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant Number 42225606, RGMA DOE Grant DE-SC0023228, the National Natural Science Foundation of China under Grant Number 42405051, the National Key R&D Program of China under Grant Number 2023YFF0805102, 2023YFF0805200, and the Taishan Scholars Program (No. tsqn202306298 and No. tsqn202306299).

Author information

Authors and Affiliations

  1. Department of Earth, Environmental, and Planetary Sciences, Brown University, Providence, RI, USA

    Sheng Wu & Emanuele Di Lorenzo

  2. Laboratory for Climate and Ocean-Atmosphere Studies & Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China

    Sheng Wu & Yonggang Liu

  3. Laoshan Laboratory, Qingdao, China

    Yingying Zhao & Daoxun Sun

  4. Physical Sciences Laboratory, NOAA, Boulder, CO, USA

    Matthew Newman & Antonietta Capotondi

  5. Atmospheric Science Program, Department of Geography, Ohio State University, Columbus, OH, USA

    Zhengyu Liu

  6. Bren School of Environmental Science and Management, University of California, Santa Barbara, CA, USA

    Samantha Stevenson

  7. Institute of Ocean Research, Peking University, Beijing, China

    Yonggang Liu

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Contributions

S.W. and E.D.L. conceptualized and led the work; M.N., Z.L., and Y.Z. conducted the analysis in discussion; S.W. and E.D.L. wrote the manuscript; S.W., Y.Z., and D.S. contributed to the LIM analysis; A.C., S.S., and Y.L. contributed to interpreting results and improving this paper.

Corresponding author

Correspondence to Emanuele Di Lorenzo.

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Wu, S., Di Lorenzo, E., Zhao, Y. et al. Decomposition of pacific decadal oscillation sheds light on its dominant modes and future response using linear inverse model. npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-025-01315-2

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  • Received: 28 April 2025

  • Accepted: 25 December 2025

  • Published: 10 January 2026

  • DOI: https://doi.org/10.1038/s41612-025-01315-2

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