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Reconstruction of a new ENSO prediction model by identifying causal factors based on an improved TriBA topological structure genetic algorithm

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Abstract

To address the inaccuracy of long-term El Niño-Southern Oscillation (ENSO) predictions, a new ENSO dynamical-statistical prediction model is established based on the combination of model reconstruction and an improved TriBA parallel genetic algorithm. We also introduce the information flow method, which is used to clarify the cause-and-effect relationship between time series, to allow forecast factors to be found in a scientific manner and improve mid- and long-term forecast results. Using this ENSO index dynamical-statistical prediction model, sea surface temperature anomalies (SSTAs) in the equatorial eastern Pacific and El Niño and La Niña events are predicted. The results show that our model has good real-time prediction ability for up to a 14-month lead time. It is shown that the overall ENSO prediction ability of our model is very good across a 60-year period. Compared with six mature models, the root mean square error (RMSE) and the correlation of the improved model are slightly worse than those of the European Centre for Medium-Range Weather Forecasts (ECMWF) model but better than those of the other five models. In addition, the gap between the summer and winter prediction results is not large, which suggests that the “spring prediction barrier” can be overcome to a certain extent. Our model represents a new technique for the prediction and exploration of ENSO.

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Data availability

The datasets generated during and/or analyzed during the current study are available as follows: The sea surface temperature anomaly (SSTA) in the Niño 3.4 region, Pacific-North America (PNA), and the Southern Oscillation index (SOI) data was obtained from the US CPC website (http://www.cpc.necp.noaa.gov/data/indices). The sea surface height (SSH) data were obtained from the Copernicus marine environment monitoring service (http://marine.copernicus.eu/services-portfolio/access-to-products/). Zonal winds, sea level pressures (SLP), and outgoing longwave radiation (OLR) data were obtained from the National Centers for Environmental Prediction (NECP) and National Centre for Atmospheric Research (NCAR) reanalysis (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html).

Code availability

Not applicable.

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Funding

This work was supported by the Chinese National Natural Science Fund (No. 41875061; No. 41775165; 51609254) and the Chinese National Natural Science Fund (2020JJ4661) of Hunan Province.

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Authors and Affiliations

  1. Institute of Meteorology and Oceanography, National University of Defence Technology, Changsha, 410073, China

    Mei Hong, Jian Shi, Yongchui Zhang, Kefeng Liu & Ren Zhang

  2. Collaborative Innovation Centre On Forecast and Evaluation of Meteorological Disaster, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Mei Hong & Ren Zhang

  3. Nanjing University of Information Science & Technology, Nanjing, 210014, China

    Mei Hong, Jian Shi, Yongchui Zhang, Kefeng Liu & Ren Zhang

Authors
  1. Mei Hong
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  2. Jian Shi
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  3. Yongchui Zhang
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  4. Kefeng Liu
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  5. Ren Zhang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jian Shi, Yongchui Zhang, and Kefeng Liu. The first draft of the manuscript was written by Mei Hong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jian Shi or Yongchui Zhang.

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Hong, M., Shi, J., Zhang, Y. et al. Reconstruction of a new ENSO prediction model by identifying causal factors based on an improved TriBA topological structure genetic algorithm. Theor Appl Climatol 150, 521–536 (2022). https://doi.org/10.1007/s00704-022-04161-x

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