Abiodun, O. I., A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, 2018: State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938, https://doi.org/10.1016/j.heliyon.2018.e00938. |
Barnett, T. P., N. Graham, S. Pazan, W. White, M. Latif, and M. Flügel, 1993: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean–atmosphere model. J. Climate, 6(8), 1545−1566, https://doi.org/10.1175/1520-0442(1993)006<1545:EAERPP>2.0.CO;2. |
Barnston, A. G., M. K. Tippett, M. L. L'Heureux, S. H. Li, and D. G. DeWitt, 2012: Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing? Bull. Amer. Meteor. Soc., 93(5), 631−651, https://doi.org/10.1175/BAMS-D-11-00111.1. |
Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97(3), 163−172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2. |
Cane, M. A., and S. E. Zebiak, 1985: A theory for El Niño and the Southern Oscillation. Science, 228(4703), 1085−1087, https://doi.org/10.1126/science.228.4703.1085. |
Cane, M. A., S. E. Zebiak, and S. C. Dolan, 1986: Experimental forecasts of El Niño. Nature, 321(6073), 827−832, https://doi.org/10.1038/321827a0. |
Chen, D., S. E. Zebiak, A. J. Busalacchi, and M. A. Cane, 1995: An improved procedure for El Niño forecasting: Implications for predictability. Science, 269(5231), 1699−1702, https://doi.org/10.1126/science.269.5231.1699. |
Duchi, J., E. Hazan, and Y. Singer, 2011: Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12, 2121−2159. |
Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J.Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 1937−1958, https://doi.org/10.5194/gmd-9-1937-2016. |
Feng, L. C., R.-H. Zhang, B. Yu, and X. Han, 2020: The roles of wind stress and subsurface cold water in the second-year cooling of the 2017/18 La Niña event. Adv. Atmos. Sci., 37, 847−860, https://doi.org/10.1007/s00376-020-0028-4. |
Gao, C., and R.-H. Zhang, 2017: The roles of atmospheric wind and entrained water temperature (Te) in the second-year cooling of the 2010−12 La Niña event. Climate Dyn., 48(1−2), 597−617, https://doi.org/10.1007/s00382-016-3097-4. |
Gao, C., R.-H. Zhang, X. R. Wu, and J. C. Sun, 2018: Idealized experiments for optimizing model parameters using a 4D-Variational method in an intermediate coupled model of ENSO. Adv. Atmos. Sci., 35, 410−422, https://doi.org/10.1007/s00376-017-7109-z. |
Goddard, L., S. J. Mason, S. E. Zebiak, C. F. Ropelewski, R. Basher, and M. A. Cane, 2001: Current approaches to seasonal to interannual climate predictions. International Journal of Climatology, 21(9), 1111−1152, https://doi.org/10.1002/joc.636. |
Guo, Y. N., X. O. Cao, B. N. Liu, and K. C. Peng, 2020: El Niño index prediction using deep learning with ensemble empirical mode decomposition. Symmetry, 12(6), 893, https://doi.org/10.3390/sym12060893. |
Ham, Y. G., J. H. Kim, and J. J. Luo, 2019: Deep learning for multi-year ENSO forecasts. Nature, 573(7775), 568−572, https://doi.org/10.1038/s41586-019-1559-7. |
Hasselmann, K., 1988: PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns. J. Geophys. Res.: Atmos., 93(D9), 11015−11021, https://doi.org/10.1029/JD093iD09p11015. |
Hirst, A. C., 1986: Unstable and damped equatorial modes in simple coupled ocean-atmosphere models. Journal of Atmospheric Sciences, 43(6), 606−632, https://doi.org/10.1175/1520-0469(1986)043<0606:UADEMI>2.0.CO;2. |
Hochreiter, S., and J. Schmidhuber, 1997: Long short-term memory. Neural Computation, 9(8), 1735−1780, https://doi.org/10.1162/neco.1997.9.8.1735. |
Irrgang, C., N. Boers, M. Sonnewald, E. A. Barnes, C. Kadow, J. Staneva, and J. Saynisch-Wagner, 2021: Towards neural earth system modelling by integrating artificial intelligence in earth system science. Nature Machine Intelligence, 3(8), 667−674, https://doi.org/10.1038/s42256-021-00374-3. |
Jin, E. K., and Coauthors, 2008: Current status of ENSO prediction skill in coupled ocean–atmosphere models. Climate Dyn., 31(6), 647−664, https://doi.org/10.1007/s00382-008-0397-3. |
Latif, M., and Coauthors, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res.: Oceans, 103(C7), 14375−14393, https://doi.org/10.1029/97JC03413. |
LeCun, Y., and Y. Bengio, 1995: Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, Cambridge, MA, United States, MIT Press, 255−258. |
McCreary, J. P. Jr., and D. L. T. Anderson, 1991: An overview of coupled ocean-atmosphere models of El Niño and the Southern Oscillation. J. Geophys. Res.: Oceans, 96(S01), 3125−3150, https://doi.org/10.1029/90JC01979. |
McPhaden, M. J., S. E. Zebiak, and M. H. Glantz, 2006: ENSO as an integrating concept in earth science. Science, 314(5806), 1740−1745, https://doi.org/10.1126/science.1132588. |
Mu, B., B. Qin, and S. J. Yuan, 2021: ENSO-ASC 1.0.0: ENSO deep learning forecast model with a multivariate air–sea coupler. Geoscientific Model Development, 14, 6977−6999, https://doi.org/10.5194/gmd-14-6977-2021. |
Nooteboom, P. D., Q. Y. Feng, C. López, E. Hernández-García, and H. A. Dijkstra, 2018: Using network theory and machine learning to predict El Niño. Earth System Dynamics, 9(3), 969−983, https://doi.org/10.5194/esd-9-969-2018. |
Philander, S. G., 1999: A review of tropical ocean–atmosphere interactions. Tellus B, 51(1), 71−90, https://doi.org/10.3402/tellusb.v51i1.16261. |
Pratt, L. Y., J. Mostow, and C. A. Kamm, 1991: Direct transfer of learned information among neural networks. Proc. 9th National Conf. on Artificial Intelligence, Anaheim, California, AAAI Press, 584−589. |
Reichstein, M., G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat, 2019: Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195−204, https://doi.org/10.1038/s41586-019-0912-1. |
Scarselli, F., and A. C. Tsoi, 1998: Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results. Neural Networks, 11(1), 15−37, https://doi.org/10.1016/S0893-6080(97)00097-X. |
Tang, Y., and W. Hsieh, 2002: Hybrid coupled models of the tropical Pacific–II ENSO prediction. Climate Dyn., 19(3), 343−353, https://doi.org/10.1007/s00382-002-0231-2. |
Tang, Y. M., and Coauthors, 2018: Progress in ENSO prediction and predictability study. National Science Review, 5(6), 826−839, https://doi.org/10.1093/nsr/nwy105. |
Tangang, F. T., W. W. Hsieh, and B. Tang, 1997: Forecasting the equatorial Pacific sea surface temperatures by neural network models. Climate Dyn., 13(2), 135−147, https://doi.org/10.1007/s003820050156. |
Tippett, M. K., A. G. Barnston, and S. H. Li, 2012: Performance of recent multimodel ENSO forecasts. J. Appl. Meteorol. Climatol., 51(3), 637−654, https://doi.org/10.1175/JAMC-D-11-093.1. |
Varotsos, C. A., C. G. Tzanis, and N. V. Sarlis, 2016: On the progress of the 2015–2016 El Niño event. Atmospheric Chemistry and Physics, 16(4), 2007−2011, https://doi.org/10.5194/acp-16-2007-2016. |
Von Storch, H., T. Bruns, I. Fischer-Bruns, and K. Hasselmann, 1988: Principal oscillation pattern analysis of the 30- to 60-day oscillation in general circulation model equatorial troposphere. J. Geophys. Res.: Atmos., 93(D9), 11022−11036, https://doi.org/10.1029/JD093iD09p11022. |
Wang, C. Z., 2019: Three-ocean interactions and climate variability: A review and perspective. Climate Dyn., 53(7), 5119−5136, https://doi.org/10.1007/s00382-019-04930-x. |
Wang, S., L. Mu, and D. R. Liu, 2021: A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder. Computers & Geosciences, 149, 104695, https://doi.org/10.1016/j.cageo.2021.104695. |
Wu, A. M., W. W. Hsieh, and B. Y. Tang, 2006: Neural network forecasts of the tropical Pacific sea surface temperatures. Neural Networks, 19(2), 145−154, https://doi.org/10.1016/j.neunet.2006.01.004. |
Xu, G. J., and Coauthors, 2019: Oceanic eddy identification using an AI scheme. Remote Sensing, 11(11), 1349, https://doi.org/10.3390/rs11111349. |
Xu, J. S., 1990: Analysis and prediction of the El Niño Southern Oscillation phenomenon using principal oscillation pattern analysis. PhD dissertation, University of Hamburg. |
Yan, J. N., L. Mu, L. Z. Wang, R. Ranjan, and A. Y. Zomaya, 2020: Temporal convolutional networks for the advance prediction of ENSO. Scientific Reports, 10(1), 8055, https://doi.org/10.1038/s41598-020-65070-5. |
You, Y. J., and J. C. Furtado, 2018: The South Pacific meridional mode and its role in tropical Pacific climate variability. J. Climate, 31(24), 10141−10163, https://doi.org/10.1175/JCLI-D-17-0860.1. |
Zebiak, S. E., and M. A. Cane, 1987: A model El Niño–Southern oscillation. Mon. Wea. Rev., 115(10), 2262−2278, https://doi.org/10.1175/1520-0493(1987)115<2262:AMENO>2.0.CO;2. |
Zhang, R.-H., and C. Gao, 2016: The IOCAS intermediate coupled model (IOCAS ICM) and its real-time predictions of the 2015–2016 El Niño event. Science Bulletin, 61(13), 1061−1070, https://doi.org/10.1007/s11434-016-1064-4. |
Zhang, R.-H., L. M. Rothstein, and A. J. Busalacchi, 1998: Origin of upper-ocean warming and El Niño change on decadal scales in the tropical Pacific Ocean. Nature, 391(6670), 879−883, https://doi.org/10.1038/36081. |
Zhang, R.-H., S. E. Zebiak, R. Kleeman, and N. Keenlyside, 2005: Retrospective El Niño forecasts using an improved intermediate coupled model. Mon. Wea. Rev., 133(9), 2777−2802, https://doi.org/10.1175/MWR3000.1. |
Zhang, R.-H., and Coauthors, 2020: A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China. Journal of Oceanology and Limnology, 38(4), 930−961, https://doi.org/10.1007/s00343-020-0157-8. |
Zhang, S. W., H. Wang, H. Jiang, and W. T. Ma, 2021: Evaluation of ENSO prediction skill changes since 2000 based on multimodel hindcasts. Atmosphere, 12(3), 365, https://doi.org/10.3390/atmos12030365. |
Zheng, G., X. F. Li, R.-H. Zhang, and B. Liu, 2020: Purely satellite data–driven deep learning forecast of complicated tropical instability waves. Science Advances, 6(29), eaba1482, https://doi.org/10.1126/sciadv.aba1482. |