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<p>The El Niño-Southern Oscillation (ENSO) is a naturally recurring interannual climate fluctuation that affects the global climate system. The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts, resulting in significant progress. Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts, reducing the forecasting skills. To this end, we propose a deep residual-coupled model prediction (Res-CMP) model, which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction. The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model (CESM) for effective prediction of the Niño 3.4 index. We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data. After determining the optimal configuration, which included selecting a suitable transfer learning rate during training, along with input variables and CESM forecast lengths, Res-CMP demonstrated a high correlation ability for 19-month lead time predictions (correlation coefficients exceeding 0.5). The Res-CMP model also alleviated the spring predictability barrier (SPB). When validated against actual ENSO events, Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Nina events (1998/99 and 2020/21) and El Niño events (2009/10 and 2015/16). Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.</p>

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A deep residual intelligent model for ENSO prediction by incorporating coupled model forecast data

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Chunyang Song,
  • Xuefeng Zhang,
  • Xingrong Chen,
  • Hua Jiang,
  • Liang Zhang,
  • Yongyong Huang

摘要

The El Niño-Southern Oscillation (ENSO) is a naturally recurring interannual climate fluctuation that affects the global climate system. The advent of deep learning-based approaches has led to transformative changes in ENSO forecasts, resulting in significant progress. Most deep learning-based ENSO prediction models which primarily rely solely on reanalysis data may lead to challenges in intensity underestimation in long-term forecasts, reducing the forecasting skills. To this end, we propose a deep residual-coupled model prediction (Res-CMP) model, which integrates historical reanalysis data and coupled model forecast data for multiyear ENSO prediction. The Res-CMP model is designed as a lightweight model that leverages only short-term reanalysis data and nudging assimilation prediction results of the Community Earth System Model (CESM) for effective prediction of the Niño 3.4 index. We also developed a transfer learning strategy for this model to overcome the limitations of inadequate forecast data. After determining the optimal configuration, which included selecting a suitable transfer learning rate during training, along with input variables and CESM forecast lengths, Res-CMP demonstrated a high correlation ability for 19-month lead time predictions (correlation coefficients exceeding 0.5). The Res-CMP model also alleviated the spring predictability barrier (SPB). When validated against actual ENSO events, Res-CMP successfully captured the temporal evolution of the Niño 3.4 index during La Nina events (1998/99 and 2020/21) and El Niño events (2009/10 and 2015/16). Our proposed model has the potential to further enhance ENSO prediction performance by using coupled models to assist deep learning methods.