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The El Niño–Southern Oscillation (ENSO) is the dominant and most consequential climate variation on Earth, and is characterized by warming of equatorial Pacific sea surface temperatures (SSTs) during the El Niño phase and cooling during the La Niña phase. ENSO events tend to have a centre—corresponding to the location of the maximum SST anomaly—in either the central equatorial Pacific (5° S–5° N, 160° E–150° W) or the eastern equatorial Pacific (5° S–5° N, 150°–90° W); these two distinct types of ENSO event are referred to as the CP-ENSO and EP-ENSO regimes, respectively. How the ENSO may change under future greenhouse warming is unknown, owing to a lack of inter-model agreement over the response of SSTs in the eastern equatorial Pacific to such warming. Here we find a robust increase in future EP-ENSO SST variability among CMIP5 climate models that simulate the two distinct ENSO regimes. We show that the EP-ENSO SST anomaly pattern and its centre differ greatly from one model to another, and therefore cannot be well represented by a single SST ‘index’ at the observed centre. However, although the locations of the anomaly centres differ in each model, we find a robust increase in SST variability at each anomaly centre across the majority of models considered. This increase in variability is largely due to greenhouse-warming-induced intensification of upper-ocean stratification in the equatorial Pacific, which enhances ocean–atmosphere coupling. An increase in SST variance implies an increase in the number of ‘strong’ EP-El Niño events (corresponding to large SST anomalies) and associated extreme weather events.
Properties of the observed ENSO diversity, the associated CP and EP regimes, and the nonlinear Bjerknes feedback a, b, The diversity means that the pattern of any ENSO event may be reconstructed by a combination of the first (a) and second (b) principal pattern from an EOF analysis on monthly SST anomalies (colour scale) and the associated wind-stress vectors (scale shown top right). The associated monthly PC time series are used to describe their evolution, and the CP- and EP-ENSO regimes by the C-index ((PC1+PC2)/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\rm{PC}}1+{\rm{PC}}2)/\sqrt{2}$$\end{document}) and E-index ((PC1-PC2)/2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$({\rm{PC}}1-{\rm{PC}}2)/\sqrt{2}$$\end{document}), respectively. c, d, The anomaly pattern associated with the EP-ENSO (c) and CP-ENSO (d) for December–February (DJF), the season in which ENSO events typically mature. e, f, Response to the E-index (e) or C-index (f) of monthly zonal wind-stress (Tauu) anomalies (in units of N m⁻²) at the anomaly centre (see Methods) associated with the E- or C-index, respectively. The monthly wind-stress anomalies were binned in 0.25-s.d. E- or C-index intervals, and the median wind-stress anomaly and index are identified for each bin (circles). A separate linear regression was carried out for positive (red) and negative (blue) median index values. The ratio of the slope for the positive indices (S2) over that for the negative indices (S1) is taken as an indication of the nonlinear Bjerknes feedback, which operates in the EP-ENSO.
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ARTICLE https://doi.org/10.1038/s41586-018-0776-9
Increased variability of eastern Pacific
El Niño under greenhouse warming
Wenju Cai1,2*, Guojian Wang1,2, Boris Dewitte3,4,5,6, Lixin Wu1*, Agus Santoso2,7, Ken Takahashi8, Yun Yang9, Aude Carréric6 &
Michael J. McPhaden10
The El Niño–Southern Oscillation (ENSO) is the dominant and most consequential climate variation on Earth, and is
characterized by warming of equatorial Pacific sea surface temperatures (SSTs) during the El Niño phase and cooling
during the La Niña phase. ENSO events tend to have a centre—corresponding to the location of the maximum SST
anomaly—in either the central equatorial Pacific (5° S–5° N, 160° E–150° W) or the eastern equatorial Pacific (5° S–5° N,
150°–90° W); these two distinct types of ENSO event are referred to as the CP-ENSO and EP-ENSO regimes, respectively.
How the ENSO may change under future greenhouse warming is unknown, owing to a lack of inter-model agreement over
the response of SSTs in the eastern equatorial Pacific to such warming. Here we find a robust increase in future EP-ENSO
SST variability among CMIP5 climate models that simulate the two distinct ENSO regimes. We show that the EP-ENSO
SST anomaly pattern and its centre differ greatly from one model to another, and therefore cannot be well represented
by a single SST ‘index’ at the observed centre. However, although the locations of the anomaly centres differ in each
model, we find a robust increase in SST variability at each anomaly centre across the majority of models considered. This
increase in variability is largely due to greenhouse-warming-induced intensification of upper-ocean stratification in the
equatorial Pacific, which enhances ocean–atmosphere coupling. An increase in SST variance implies an increase in the
number of ‘strong’ EP-El Niño events (corresponding to large SST anomalies) and associated extreme weather events.
Alternating between El Niño and La Niña events, the ENSO affects
extreme weather events, ecosystems and agriculture around the
world1–7. ENSO events vary greatly8–15: the EP-ENSO is associated with
strong El Niño events and weak cold SST anomalies, and is character-
ized by the maximum SST anomaly (the SST anomaly centre) being
located in the eastern equatorial Pacific (the ‘Niño3’ region: 5°S–5°N,
150°–90°W); the CP-ENSO is associated with strong or moderate La
Niña events and modest El Niño events, and is characterized by the
SST anomaly centre being located in the central equatorial Pacific
(5°S–5°N, 160°E–150°W). EP-El Niño events are the strongest and
most destructive El Niño events. During such events, SST warming in
the Niño3 region leads to flooding in southwest USA, Ecuador and
northeast Peru, and to droughts in regions that border the western
Pacific1,4. In extreme cases, the disruption includes substantial loss of
marine life in the eastern Pacific, mass bleaching of corals across the
Pacific and beyond
2
, and movement of the intertropical convergence
zone7 and of the South Pacific convergence zone towards the equa-
tor
5,16
, inducing catastrophic floods and droughts across the Pacific
region
5,7
. Because of these severe effects, determining how EP-El Niño
SST variability responds to greenhouse warming is one of the most
important issues in climate science. However, over several model gen-
erations, there has been no inter-model consensus on future variability
using conventional ENSO indices1719.
This lack of consensus is despite inter-model agreement on the
change in mean state and modest inter-model agreement on the
response of CP-ENSO SST variability and on the change in certain
characteristics of ENSO extremes. First, faster warming in the eastern
equatorial Pacific than in the surrounding regions and in the equatorial
Pacific than in the non-equatorial Pacific
20
facilitates an increased fre-
quency of equatorward shifts of the convergence zones and increased
rainfall variability, even if SST variability does not change
7,16,21
. The
extreme shifts of the convergence zones occur during El Niño events,
particularly during strong ones5,7,16. Second, El Niño events with
eastward-propagating anomalies increase in frequency as a conse-
quence of weakening Walker circulation22. Finally, there has been
a focus on the response of CP-ENSO SST variability to greenhouse
warming. Although the frequency of CP-El Niño events is projected to
increase, the robustness of the increased frequency is debated9,2325. On
the other hand, a projected faster warming in the surface layer of the
ocean than at depths enhances the role of the relatively cold subsurface
water in the central Pacific in generating strong La Niña events, leading
to an increased frequency of extreme La Niña events under greenhouse
warming26.
The response of EP-ENSO SST variability to greenhouse warming is
even more uncertain, owing to the lack of inter-model consensus
1719
.
Previous examinations of this issue focused on SST variability at a fixed
location, typically the Niño3 region17,18. This approach assumes that
models simulate an EP-ENSO SST anomaly centre that can be repre-
sented by the Niño3 SST index, as is the case in observations. Here
we show that the longitude of EP-ENSO SST anomaly centres differs
greatly from one model to another, particularly when considering
models that cannot simulate ENSO diversity. However, we also show
that there is a robust increase across models in EP-El Niño SST varia-
bility at the anomaly centre of each model.
1Key Laboratory of Physical Oceanography, Institute for Advanced Ocean Studies, Ocean University of China and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China.
2Centre for Southern Hemisphere Oceans Research (CSHOR), CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia. 3Centro de Estudios Avanzados en Zonas Áridas, Coquimbo, Chile.
4Departamento de Biología, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile. 5Millennium Nucleus for Ecology and Sustainable Management of Oceanic Islands,
Coquimbo, Chile. 6Laboratoire d’Etudes en Géophysique et Océanographie Spatiales, Toulouse, France. 7Australian Research Council Centre of Excellence for Climate Extremes, The University of
New South Wales, Sydney, New South Wales, Australia. 8Servicio Nacional de Meteorología e Hidrología del Perú—SENAMHI, Lima, Peru. 9College of Global Change and Earth System Science,
Beijing Normal University and University Corporation for Polar Research, Beijing, China. 10NOAA/Pacific Marine Environmental Laboratory, Seattle, WA, USA. *e-mail: wenju.cai@csiro.au;
lxwu@ouc.edu.cn
13 DECEMBER 2018 | VOL 564 | NATURE | 201
© 2018 Springer Nature Limited. All rights reserved.
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