Changing El Niño–Southern Oscillation in a warming climate
2021
Cai, Wenju | Santoso, Agus | Collins, Matthew | Dewitte, Boris | Karamperidou, Christina | Kug, Jong-seong | Lengaigne, Matthieu | Mcphaden, Michael J. | Stuecker, Malte F. | Taschetto, Andréa S. | Timmermann, Axel | Wu, Lixin | Yeh, Sang-wook | Wang, Guojian | Ng, Benjamin | Jia, Fan | Yang, Yun | Ying, Jun | Zheng, Xiao-tong | Bayr, Tobias | Brown, Josephine R. | Capotondi, Antonietta | Cobb, Kim M. | Gan, Bolan | Geng, Tao | Ham, Yoo-geun | Jin, Fei-fei | Jo, Hyun-su | Li, Xichen | Lin, Xiaopei | Mcgregor, Shayne | Park, Jae-heung | Stein, Karl | Yang, Kai | Zhang, Li | Zhong, Wenxiu
Originating in the equatorial Pacific, the El Niño–Southern Oscillation (ENSO) has highly consequential global impacts, motivating the need to understand its responses to anthropogenic warming. In this Review, we synthesize advances in observed and projected changes of multiple aspects of ENSO, including the processes behind such changes. As in previous syntheses, there is an inter-model consensus of an increase in future ENSO rainfall variability. Now, however, it is apparent that models that best capture key ENSO dynamics also tend to project an increase in future ENSO sea surface temperature variability and, thereby, ENSO magnitude under greenhouse warming, as well as 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 palaeoclimate evidence of stronger ENSO variability since the 1950s compared with past centuries. The increase in ENSO variability, though underpinned by increased equatorial Pacific upper-ocean stratification, is strongly influenced by internal variability, raising issues about its quantifiability and detectability. Yet, ongoing coordinated community efforts and computational advances are enabling long-simulation, large-ensemble experiments and high-resolution modelling, offering encouraging prospects for alleviating model biases, incorporating fundamental dynamical processes and reducing uncertainties in projections.
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