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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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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
, and movement of the intertropical convergence
zone7 and of the South Pacific convergence zone towards the equa-
, inducing catastrophic floods and droughts across the Pacific
. 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
facilitates an increased fre-
quency of equatorward shifts of the convergence zones and increased
rainfall variability, even if SST variability does not change
. 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
The response of EP-ENSO SST variability to greenhouse warming is
even more uncertain, owing to the lack of inter-model consensus
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:;
13 DECEMBER 2018 | VOL 564 | NATURE | 201
© 2018 Springer Nature Limited. All rights reserved.
... This agrees well with our results, although we do not see the increase in CP amplitude in CESM-LE, possibly due to the different time periods used and the relatively small magnitude of the change. Recently Cai et al. (2018) and Cai et al. (2021) showed that for CMIP5 and CMIP6 models that represent Figure 3. ENSO frequency in each SMILE for EP and CP El Niño events and for La Niña events (left, middle, and right columns respectively). Black line shows the ensemble mean for each year, red line shows the ensemble maximum, blue line shows the ensemble minimum, purple line represents HadISST observations, and green line is the first ensemble member. ...
... However, only two of the models they identified as able to represent ENSO well are included in our study and they differ in sign (increasing amplitude in CESM-LE; decreasing amplitude in GFDL-ESM2M). This is in agreement with Cai et al. (2018), who also found decreasing amplitude in GFDL-ESM2M, which was an outlier in their study that used single ensemble members from CMIP5. These model differences have been suggested to be related to changes in the zonal gradient of mean SST. ...
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Tropical Pacific climate varies at interannual, decadal and centennial time scales, and exerts a significant influence on global climate. Climate model projections exhibit a large spread in the magnitude and pattern of tropical Pacific warming in response to greenhouse-gas forcing. Here, we show that part of this spread can be explained by model biases in the simulation of interannual variability, namely the El Niño/Southern Oscillation (ENSO) phenomenon. We show that models that exhibit strong ENSO nonlinearities simulate a more accurate balance of ENSO feedbacks, and their projected tropical Pacific sea surface temperature warming pattern is closely linked to their projected ENSO response. Within this group, models with ENSO nonlinearity close to observed project stronger warming of the cold tongue, whereas models with stronger than observed ENSO nonlinearity project a more uniform warming of the tropical Pacific. These differences are also manifest in the projected changes of precipitation patterns, thereby highlighting that ENSO simulation biases may lead to potentially biased projections in long-term precipitation trends, with great significance for regional climate adaptation strategies.
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It has been previously proposed that two El Niño (EN) regimes, strong and moderate, exist but the historical observational record is too short to establish this conclusively. Here, 1200 years of simulations with the GFDL CM2.1 model allowed us to demonstrate their existence in this model and, by showing that the relevant dynamics are also evident in observations, we present a stronger case for their existence in nature. In CM2.1, the robust bimodal probability distribution of equatorial Pacific sea surface temperature (SST) indices during EN peaks provides evidence for the existence of the regimes, which is also supported by a cluster analysis of these same indices. The observations agree with this distribution, with the EN of 1982-1983 and 1997-1998 corresponding to the strong EN regime and all the other observed EN to the moderate regime. The temporal evolution of various indices during the observed strong EN agrees very well with the events in CM2.1, providing further validation of this model as a proxy for nature. The two regimes differ strongly in the magnitude of the eastern Pacific warming but not much in the central Pacific. Observations and model agree in the existence of a finite positive threshold in the SST anomaly above which the zonal wind response to warming is strongly enhanced. Such nonlinearity in the Bjerknes feedback, which increases the growth rate of EN events if they reach sufficiently large amplitude, is very likely the essential mechanism that gives rise to the existence of the two EN regimes. Oceanic nonlinear advection does not appear essential for the onset of strong EN. The threshold nonlinearity could make the EN regimes very sensitive to stochastic forcing. Observations and model agree that the westerly wind stress anomaly in the central equatorial Pacific in late boreal summer has a substantial role determining the EN regime in the following winter and it is suggested that a stochastic component at this time was key for the development of the strong EN towards the end of 1982.
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The El Nino/Southern Oscillation is Earth's most prominent source of interannual climate variability, alternating irregularly between El Nino and La Nina, and resulting in global disruption of weather patterns, ecosystems, fisheries and agriculture(1-5). The 1998-1999 extreme La Nina event that followed the 1997-1998 extreme El Nino event(6) switched extreme El Nino-induced severe droughts to devastating floods in western Pacific countries, and vice versa in the southwestern United States(4,7). During extreme La Nina events, cold sea surface conditions develop in the central Pacific(8,9), creating an enhanced temperature gradient from the Maritime continent to the central Pacific. Recent studies have revealed robust changes in El Nino characteristics in response to simulated future greenhouse warming(10-12), but how La Nina will change remains unclear. Here we present climate modelling evidence, from simulations conducted for the Coupled Model Intercomparison Project phase 5 (ref. 13), for a near doubling in the frequency of future extreme La Nina events, from one in every 23 years to one in every 13 years. This occurs because projected faster mean warming of the Maritime continent than the central Pacific, enhanced upper ocean vertical temperature gradients, and increased frequency of extreme El Nino events are conducive to development of the extreme La Nina events. Approximately 75% of the increase occurs in years following extreme El Nino events, thus projecting more frequent swings between opposite extremes from one year to the next.
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El Niño Southern Oscillation (ENSO) is a naturally occurring mode of tropical Pacific variability, with global impacts on society and natural ecosystems. While it has long been known that El Niño events display a diverse range of amplitudes, triggers, spatial patterns, and life cycles, the realization that ENSO's impacts can be highly sensitive to this event-to-event diversity is driving a renewed interest in the subject. This paper surveys our current state of knowledge of ENSO diversity, identifies key gaps in understanding, and outlines some promising future research directions.
The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°-0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s-40s when observation instruments changed rapidly. Both long- (1900-2015) and short-term (2000-15) SST trends in ERSSTv5 remain significant as in ERSSTv4.
The El Nino/Southern Oscillation (ENSO) is the dominant climate phenomenon affecting extreme weather conditions worldwide. Its response to greenhouse warming has challenged scientists for decades, despite model agreement on projected changes in mean state. Recent studies have provided new insights into the elusive links between changes in ENSO and in the mean state of the Pacific climate. The projected slow-down in Walker circulation is expected to weaken equatorial Pacific Ocean currents, boosting the occurrences of eastward-propagating warm surface anomalies that characterize observed extreme El Nino events. Accelerated equatorial Pacific warming, particularly in the east, is expected to induce extreme rainfall in the eastern equatorial Pacific and extreme equatorward swings of the Pacific convergence zones, both of which are features of extreme El Nino. The frequency of extreme La Nina is also expected to increase in response to more extreme El Ninos, an accelerated maritime continent warming and surface-intensified ocean warming. ENSO-related catastrophic weather events are thus likely to occur more frequently with unabated greenhouse-gas emissions. But model biases and recent observed strengthening of the Walker circulation highlight the need for further testing as new models, observations and insights become available.