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1. Seasonality of Pacific Decadal Oscillation Prediction Skill

   https://doi.org/10.1029/2025GL116122  

   Meeker, E D; Maroon, E A; Deppenmeier, A L; Thompson, L A; Vimont, D J; Yeager, S G (July 2025, Geophysical Research Letters)

   Abstract We investigate coupled climate model initialized predictions of the Pacific Decadal Oscillation (PDO) prediction skill in the Community Earth System Model (CESM) Seasonal to Multi Year Large Ensemble (SMYLE). The PDO is predictable up to a year in advance in SMYLE; however, the predictability depends on verification month, with skill degrading most rapidly in boreal spring for all initializations. To examine the role of teleconnections from El Niño–Southern Oscillation (ENSO) in the prediction skill of the PDO, we use a multi‐linear regression model. The linear model shows that initial value persistence explains most of the PDO prediction skill in SMYLE. In addition, the PDO prediction skill's seasonal dependence is fully reproduced only when ENSO is included as a predictor. These results suggest that ENSO has a strong influence on the seasonality of PDO predictions. 

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2. Pacific Decadal Oscillation: Tropical Pacific Forcing versus Internal Variability

   https://doi.org/10.1175/JCLI-D-18-0164.1  

   Zhang, Yu; Xie, Shang-Ping; Kosaka, Yu; Yang, Jun-Chao (October 2018, Journal of Climate)
3. Interdecadal modulation of ENSO amplitude by the Atlantic multi-decadal oscillation (AMO)

   https://doi.org/10.1007/s00382-020-05408-x  

   Gong, Yuhan; Li, Tim; Chen, Lin (November 2020, Climate Dynamics)

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4. Pacific Decadal Oscillation Influences Tropical Oxygen Minimum Zone Extent and Obscures Anthropogenic Changes

   https://doi.org/10.1029/2022GL102123  

   Poupon, Mathieu A.; Resplandy, Laure; Lévy, Marina; Bopp, Laurent (April 2023, Geophysical Research Letters)
5. Oceanic Harbingers of Pacific Decadal Oscillation Predictability in CESM2 Detected by Neural Networks

   https://doi.org/10.1029/2021GL095392  

   Gordon, Emily M.; Barnes, Elizabeth A.; Hurrell, James W. (November 2021, Geophysical Research Letters)

   Abstract Predicting Pacific Decadal Oscillation (PDO) transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large‐scale climate shifts and regional climate predictability. We show that artificial neural networks (ANNs) predict PDO persistence and transitions with lead times of 12 months onward. Using layer‐wise relevance propagation to investigate the ANN predictions, we demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. For PDO transitions, ANNs recognize a build‐up of ocean heat content in the off‐equatorial western Pacific 12–27 months before a transition occurs. The results support the continued use of ANNs in climate studies where explainability tools can assist in mechanistic understanding of the climate system. 

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