Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble
Abstract
Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead. However, the predictions are under-dispersive in a state-dependent manner. Rank histograms reveal overconfidence issues for strong ENSO events and a warm bias for weak events. February predictability arises from canonical Bjerknes feedback dynamics reinforced by subtropical-tropical interactions resembling the North Pacific Meridional Mode (NPMM). The best February-initialized predictions feature warm western Pacific subsurface anomalies and La Nina anomalies at initialization, with equatorially confined anomalies and relatively weak sensitivities to extratropical influences thereafter. Second-year ENSO predictability in August-initialized predictions instead emerges through a delayed pathway characterized by cross-basin preconditioning. Skillful cases begin with a cold tropical equatorial South Atlantic and a warmer tropical western Indian Ocean, while Pacific processes play a reduced role until the following year. SMYLE matches the skill of the cross-validated XRO at both December targets, and its sensitivities agree with the observationally fitted benchmark, though the Atlantic couplings appear misrepresented. These findings suggest that improving the initial Pacific state and cross-basin coupling fidelity in GCMs may help improve ENSO prediction.
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BibTeX
@misc{mao2026precursors,
title = {{Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble}},
author = {Yudi Mao and Gan Zhang and Hui Li and Dillon J. Amaya and Stephen G. Yeager and Sen Zhao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25706},
eprint = {2609.25706},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25706},
}
RIS
TY - UNPB
TI - Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble
AU - Yudi Mao
AU - Gan Zhang
AU - Hui Li
AU - Dillon J. Amaya
AU - Stephen G. Yeager
AU - Sen Zhao
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2609.25706
UR - https://arxiv.org/abs/2609.25706
AB - Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead. However, the predictions are under-dispersive in a state-dependent manner. Rank histograms reveal overconfidence issues for strong ENSO events and a warm bias for weak events. February predictability arises from canonical Bjerknes feedback dynamics reinforced by subtropical-tropical interactions resembling the North Pacific Meridional Mode (NPMM). The best February-initialized predictions feature warm western Pacific subsurface anomalies and La Nina anomalies at initialization, with equatorially confined anomalies and relatively weak sensitivities to extratropical influences thereafter. Second-year ENSO predictability in August-initialized predictions instead emerges through a delayed pathway characterized by cross-basin preconditioning. Skillful cases begin with a cold tropical equatorial South Atlantic and a warmer tropical western Indian Ocean, while Pacific processes play a reduced role until the following year. SMYLE matches the skill of the cross-validated XRO at both December targets, and its sensitivities agree with the observationally fitted benchmark, though the Atlantic couplings appear misrepresented. These findings suggest that improving the initial Pacific state and cross-basin coupling fidelity in GCMs may help improve ENSO prediction.
ER -