How Does El Niño–Southern Oscillation Change Under Global Warming—A First Look at CMIP6

Fredriksen, Hege‐Beate; Berner, Judith; Subramanian, Aneesh; Capotondi, Antonietta

doi:10.1029/2020gl090640

Cited by 96 publications

(98 citation statements)

References 35 publications

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“…The result is consistent between scenarios: the signal is stronger for the RCP8.5 scenario than for the RCP4.5. In contrast, most of CMIP6 models under SSP5-8.5 scenario project an increase in ENSO amplitude towards the end of the twenty-first century, in agreement with recent studies (Fredriksen et al 2020). The strongest increase is projected by models with high interannual and decadal ENSO variability.…”

Section: Summary and Discussionsupporting

confidence: 89%

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

et al. 2021

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There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

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Section: Summary and Discussionsupporting

confidence: 89%

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

et al. 2021

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“…Kohyama et al (2017), however, suggest that a La Niña like warming pattern (i.e. the western Pacific warms more than the eastern Pacific, resulting in an increased zonal SST gradient), should result in a decrease in ENSO amplitude as does Fredriksen et al (2020). This result is in agreement with an observed decrease in tropical Pacific variability coupled with an increase in the trade winds and increase in thermocline tilt from 2000-2011 as compared to 1979-1999 (Hu et al, 2013).…”

Section: Can the Observed Increase In Frequency Of Cp Niños Be Explained By Internal Variability And What Does This Implysupporting

confidence: 79%

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Maher

Tabarin²,

Milinski

2022

Preprint

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Abstract. The El Niño Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño) and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: Central Pacific (CP) and Eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and reanalysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similar to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs. When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and SST pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models due to the large spread of results found within a single model due to internal variability alone.

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“…CP ENSO variance is also anticipated to increase, but only in 59% of models 36 . CMIP6 models also support these findings 110 . For instance, 100% of 23 CMIP6 models indicate enhanced EP ENSO variance in the future (fig.…”

Section: Increased Enso Sst Variabilitymentioning

confidence: 61%

“…27 ). Such increasing frequency is also seen in CMIP6 models 26,110 and continues for as long as a century after global mean temperature stabilizes at 1.5-2.0 °C warming relative to the pre-industrial level 28,109 .…”

Section: Projected Enso Changesmentioning

confidence: 64%

Changing El Niño–Southern Oscillation in a warming climate

Cai

Santoso

Collins

et al. 2021

Nat Rev Earth Environ

Self Cite

318

207

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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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How Does El Niño–Southern Oscillation Change Under Global Warming—A First Look at CMIP6

Cited by 96 publications

References 35 publications

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

Changing El Niño–Southern Oscillation in a warming climate

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