Cai, W., A. Santoso, M. Collins, B. Dewitte, C. Karamperidou, J.-S. Kug, M. Lengaigne, M.J. McPhaden, M.F. Stuecker, A.S. Taschetto, A. Timmermann, L. Wu, S.-W. Yeh, G. Wang, B. Ng, F. Jia, Y. Yang, J. Ying, X.-T. Zheng, T. Bayr, J.R. Brown, A. Capotondi, K.M. Cobb, B. Gan, T. Geng, Y.-G. Ham, F.-F. Jin, H.-S. Jo, X. Li, X. Lin, S. McGregor, J.-H. Park, K. Stein, K. Yang, L. Zhang, and W. Zhong (2021): Changing El Niño-Southern Oscillation in a warming climate. Nature Rev. Earth Environ., doi: 10.1038/s43017-021-00199-z.
El Niño-Southern Oscillation (ENSO), which originates in the tropical Pacific ocean through feedbacks between the ocean and the atmosphere, has highly consequential global impacts that motivate the need to better understand its responses to anthropogenic warming. This review article is in part a condensation of highlights from the recent AGU book published in November 2020 entitled "El Niño Southern Oscillation in a Changing Climate”. The article synthesizes advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, it describes an inter-model consensus for increased future ENSO rainfall variability in the tropical Pacific. It also notes that the models which best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, in addition, an eastward shift and intensification of ENSO-related atmospheric teleconnections—the Pacific-North American and Pacific-South American patterns. Such projected changes are consistent with paleo-climate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability results from an increase in equatorial Pacific upper-ocean stratification, but it is also strongly influenced by internal variability, raising issues about quantifiability and detectability. Ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modeling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes, and reducing uncertainties in projections.