Deep learning for multi-year ENSO forecasts

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Abstract

Variations in the El Niño/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts1. Robust, long-lead forecasts would therefore be valuable for managing policy responses. But despite decades of effort, forecasting ENSO events at lead times of more than one year remains problematic2. Here we show that a statistical forecast model employing a deep-learning approach produces skilful ENSO forecasts for lead times of up to one and a half years. To circumvent the limited amount of observation data, we use transfer learning to train a convolutional neural network (CNN) first on historical simulations3 and subsequently on reanalysis from 1871 to 1973. During the validation period from 1984 to 2017, the all-season correlation skill of the Nino3.4 index of the CNN model is much higher than those of current state-of-the-art dynamical forecast systems. The CNN model is also better at predicting the detailed zonal distribution of sea surface temperatures, overcoming a weakness of dynamical forecast models. A heat map analysis indicates that the CNN model predicts ENSO events using physically reasonable precursors. The CNN model is thus a powerful tool for both the prediction of ENSO events and for the analysis of their associated complex mechanisms.

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Fig. 1: Architecture of the CNN model used for the ENSO forecasts.
Fig. 2: ENSO correlation skill in the CNN model.
Fig. 3: Physical interpretation of the CNN model ENSO forecasts.
Fig. 4: CNN forecast system for the prediction of El Niño type.

Data availability

Data related to this paper can be downloaded from: SODA version 2.2.4, https://climatedataguide.ucar.edu/climate-data/soda-simple-ocean-data-assimilation; GODAS, https://www.esrl.noaa.gov/psd/data/gridded/data.godas.html; ERA-Interim, https://apps.ecmwf.int/datasets/data/interim-full-daily; NMME phase 1, https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/; and The CMIP5 database, https://esgf-node.llnl.gov/projects/cmip5/.

Code availability

TensorFlow (https://www.tensorflow.org) libraries were implemented to formulate the statistical forecast model using the CNN. The code for the CNN model can be downloaded at https://doi.org/10.5281/zenodo.3244463.

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Acknowledgements

This study is funded by the Korea Meteorological Administration Research and Development Program under grant KMI2018-03214. Y.-G.H. was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1A6A1A03012647). J.-J.L. is supported by ‘The Startup Foundation for Introducing Talent’ of NUIST. We are grateful to W. Merryfield for providing comments and to T. Doi for providing part of the SINTEX-F hindcast data used in the validation.

Author information

Y.-G.H. and J.-H.K. designed the experiments and conducted the analyses. Y.-G.H. wrote most of the manuscript. J.-H.K. and Y.-G.H. performed the CNN hindcast experiments. J.-J.L. conducted the SINTEX-F hindcast experiments and reported the results. All authors discussed the study results and reviewed the manuscript.

Correspondence to Yoo-Geun Ham.

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Extended data figures and tables

Extended Data Fig. 1 Comparison of the ENSO correlation skill between the CNN model and the feed-forward neural network model.

a, The all-season correlation skill of the three-month-moving-averaged Nino3.4 index as a function of the lead months of the forecast in the CNN model (red) and the feed-forward neural network model (blue). The validation period is between 1984 and 2017. b, c, The correlation skill of the Nino3.4 index targeted to each calendar month in the CNN model (b) and in the feed-forward neural network model (c). Hatching highlights the forecasts with correlation skill exceeding 0.5. Source data

Extended Data Fig. 2 The improvement in the skill of the CNN model due to the CMIP5 dataset and the transfer learning.

a, The all-season correlation skill of Nino3.4 index at a lead of 18 months with a different number of CMIP5 samples. The red area denotes the number of available observed samples. We note that transfer learning is not applied to the series of sensitivity tests (that is, observations during the training period are not used to set up the CNN model). b, The all-season Nino3.4 correlation skill as a function of the lead months of the forecast with and without the transfer learning. The CNN model without the transfer learning is formulated by using all the CMIP5 and the observed samples during the training period (that is, 1871 to 1973) in a single training period. Therefore, the number of samples for the CNN model without the transfer learning is exactly the same with those with the transfer learning. Source data

Extended Data Fig. 3 The time series of climate indices.

ad, The time series of the IOD index (difference of the area-averaged SST over 50–70° E, 10° S–10° N from that over 90–110° E, 15°–0° S) during the SON season (a), the Indian Ocean Basin-wide warming (IOBW) index (area-averaged SST over 40–110° E, 15° S–10° N) during the JFM season (b), the Western Hemispheric Warm Pool (WHWP) index (area-averaged SST over 60–105° E, 10–35° N) during the MJJ season (c), and the Pacific Meridional Mode (PMM) index (first Maximum Covariance Analysis (MCA) principal components over the 175° E–95° W, 21° S–32° N using SST and 10-m winds) during the DJF season (d). The values preceding the 1997/98 El Niño event are denoted by the red star when the value is positive and the blue star when the value is negative. Source data

Extended Data Fig. 4 The time evolution of the 1997/98 El Niño event.

The SST (shading) and 850-hPa wind vector (vectors) at: a, MJJ 1996; b, ASO 1996; c, NDJ 1996; and d, FMA 1997. The global map is generated in Matplotlib31.

Extended Data Fig. 5 The area-averaged heat map values for El Niño events.

a, b, The area-averaged heat map for EP-type El Niño events (a), and CP-type El Niño events (b) among all El Niño events over five ocean domains (that is, south Pacific, equatorial Pacific, north Pacific, Indian Ocean and equatorial Atlantic). These areas are defined as: south Pacific, [160° E–60° W, 57.5°–17.5° S]; equatorial Pacific, [120° E–80° W, 17.5° S–22.5° N]; north Pacific [120° E–100° W, 22.5–62.5° N]; Indian Ocean, [40°–120° E, 37.5° S–22.5° N]; and equatorial Atlantic [60°–0° W, 17.5° S–22.5° N]. The horizontal dashed line denotes one standard deviation of the heat map value for the displayed El Niño events for five ocean basins. We note that only the heat maps of the El Niño events for which the type is correctly predicted in the CNN are analysed. Source data

Extended Data Fig. 6 The SST pattern developed by precursors for CP El Niño.

a, b, The SST anomalies for forecasts with leads of 12 months regressed onto the pattern regression index for CP-type El Niño precursors over the Indian Ocean (a) and the south Pacific (b) at the NDJ season. The pattern regression index for CP-type El Niño precursors is obtained by calculating the pattern regression of the NDJ SST and heat content anomalies onto the selected anomaly for CP El Niño event in Fig. 4f. The black box denotes the region in which the pattern regression index was calculated. We note that both regressed SST patterns are classified as the CP-type El Niño24. The global map is generated in Matplotlib31.

Extended Data Table 1 Details of the CMIP5 models
Extended Data Table 2 The dataset for training and validating the CNN model
Extended Data Table 3 The skill of the feed-forward neural network model with a different number of predictors
Extended Data Table 4 12-month lead forecast results of predicting the type of El Niño

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Ham, Y., Kim, J. & Luo, J. Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019) doi:10.1038/s41586-019-1559-7

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