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</o:shapelayout></xml><![endif]--></head><body lang=ES link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNormal><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/nature"><i><span lang=EN-US style='color:windowtext;text-decoration:none'>Nature</span></i></a></span><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'> <b><span lang=EN-US>volume 573</span></b><span lang=EN-US>, pages 568–572 (2019) <o:p></o:p></span></span></p><p class=MsoNormal><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7?WT.ec_id=NATURE-20190926&utm_source=nature_etoc&utm_medium=email&utm_campaign=20190926&sap-outbound-id=65009000AFAE4CFBDC842073DE65D49CD7308762&utm_source=hybris-campaign&utm_medium=email&utm_campaign=000_SKN6563_0000015362_41586-Nature-20190926-EAlert&utm_content=EN_internal_33925_20190926#article-info"><span lang=EN-US style='color:windowtext;text-decoration:none'>Published: 18 September 2019</span></a><o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'>https://www.nature.com/articles/s41586-019-1559-7?WT.ec_id=NATURE-20190926&utm_source=nature_etoc&utm_medium=email&utm_campaign=20190926&sap-outbound-id=65009000AFAE4CFBDC842073DE65D49CD7308762&utm_source=hybris-campaign&utm_medium=email&utm_campaign=000_SKN6563_0000015362_41586-Nature-20190926-EAlert&utm_content=EN_internal_33925_20190926<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><o:p> </o:p></span></p><p class=MsoNormal><b><span lang=EN-US style='font-size:24.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'>Deep learning for multi-year ENSO forecasts<o:p></o:p></span></b></p><p class=MsoNormal><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7?WT.ec_id=NATURE-20190926&utm_source=nature_etoc&utm_medium=email&utm_campaign=20190926&sap-outbound-id=65009000AFAE4CFBDC842073DE65D49CD7308762&utm_source=hybris-campaign&utm_medium=email&utm_campaign=000_SKN6563_0000015362_41586-Nature-20190926-EAlert&utm_content=EN_internal_33925_20190926#auth-1"><span lang=EN-US style='color:windowtext;text-decoration:none'>Yoo-Geun Ham</span></a></span><span lang=EN-US style='font-family:"Arial","sans-serif";mso-fareast-language:ES'>, </span><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7?WT.ec_id=NATURE-20190926&utm_source=nature_etoc&utm_medium=email&utm_campaign=20190926&sap-outbound-id=65009000AFAE4CFBDC842073DE65D49CD7308762&utm_source=hybris-campaign&utm_medium=email&utm_campaign=000_SKN6563_0000015362_41586-Nature-20190926-EAlert&utm_content=EN_internal_33925_20190926#auth-2"><span lang=EN-US style='color:windowtext;text-decoration:none'>Jeong-Hwan Kim</span></a></span><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'> <span lang=EN-US>& </span></span><span style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7?WT.ec_id=NATURE-20190926&utm_source=nature_etoc&utm_medium=email&utm_campaign=20190926&sap-outbound-id=65009000AFAE4CFBDC842073DE65D49CD7308762&utm_source=hybris-campaign&utm_medium=email&utm_campaign=000_SKN6563_0000015362_41586-Nature-20190926-EAlert&utm_content=EN_internal_33925_20190926#auth-3"><span lang=EN-US style='color:windowtext;text-decoration:none'>Jing-Jia Luo</span></a></span><span lang=EN-US style='font-family:"Arial","sans-serif";mso-fareast-language:ES'> <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US style='font-family:"Arial","sans-serif";mso-fareast-language:ES'><o:p> </o:p></span></p><p class=MsoNormal><b><span lang=EN-US style='font-size:16.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'>Abstract<o:p></o:p></span></b></p><p class=MsoNormal><b><span lang=EN-US style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'><o:p> </o:p></span></b></p><p class=MsoNormal><span lang=EN-US style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'>Variations in the El Niņo/Southern Oscillation (ENSO) are associated with a wide array of regional climate extremes and ecosystem impacts</span><sup><span style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7#ref-CR1" title="McPhaden, M. J., Zebiak, S. E. & Glantz, M. H. ENSO as an integrating concept in Earth science. Science 314, 1740–1745 (2006)."><span lang=EN-US style='color:windowtext;text-decoration:none'>1</span></a></span></sup><span lang=EN-US style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'>. 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 problematic</span><sup><span style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7#ref-CR2" title="Barnston, A. G., Tippett, M. K., L’Heureux, M. L., Li, S. & DeWitt, D. G. Skill of real-time seasonal ENSO model predictions during 2002–11: is our capability increasing? Bull. Am. Meteorol. Soc. 93, 631–651 (2012)."><span lang=EN-US style='color:windowtext;text-decoration:none'>2</span></a></span></sup><span lang=EN-US style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'>. 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 <b>convolutional neural network</b> (CNN) first on historical simulations</span><sup><span style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'><a href="https://www.nature.com/articles/s41586-019-1559-7#ref-CR3" title="Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)."><span lang=EN-US style='color:windowtext;text-decoration:none'>3</span></a></span></sup><span lang=EN-US style='font-size:12.0pt;font-family:"Arial","sans-serif";mso-fareast-language:ES'> 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.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p></div></body></html>