Editing IPCC:AR6/WGI/Chapter-3 (section)

=== 3.7.3 El Niño–Southern Oscillation (ENSO) ===

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The El Niño–Southern Oscillation (ENSO), which is generated via seasonally modulated interactions between the tropical Pacific ocean and atmosphere, influences severe weather, rainfall, river flow and agricultural production over large parts of the world ( [[#McPhaden--2006|McPhaden et al., 2006]] ). In fact, the remote climate influence of ENSO is so large that knowledge of its current phase and forecasts of its future phase largely underpin many seasonal rainfall and temperature forecasts worldwide (Annex IV.2.3).

AR5 noted that there have been clear improvements in the simulation of ENSO through previous generations of CMIP models ( [[#Flato--2013|Flato et al., 2013]] ), such that many CMIP5 models displayed behaviour that was qualitatively similar to that of the observed ENSO ( [[#Guilyardi--2012|Guilyardi et al., 2012]] ). However, systematic errors were identified in the models’ representation of the tropical Pacific mean state and aspects of their interannual variability that affect quantitative comparisons. The AR5 assessment of ENSO concluded that the considerable observed inter-decadal modulations in ENSO amplitude and spatial pattern were largely consistent with unforced model simulations. Thus, there was ''low confidence'' in the role of a human-induced influence in these ( [[#Bindoff--2013|Bindoff et al., 2013]] ).

Observed ENSO amplitude, which is measured by the standard deviation of SST anomalies in a central equatorial Pacific region often referred to as the Nino 3.4 region, along with the lifecycle of events, are both reasonably well reproduced by most CMIP5 and CMIP6 models (Figure 3.36; [[#Bellenger--2014|Bellenger et al., 2014]] ; [[#Planton--2021|Planton et al., 2021]] ). The average CMIP5 model ENSO amplitude is slightly lower than that observed, while the average CMIP6 model ENSO amplitude is slightly higher than observed (Figure 3.36). The ENSO amplitude of the individual models, however, is highly variable across CMIP5 and CMIP6 models with many displaying either more or less variability than observed ( [[#Stevenson--2012|Stevenson, 2012]] ; [[#Grose--2020|Grose et al., 2020]] ; [[#Planton--2021|Planton et al., 2021]] ).

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[[File:58c66b95fe275cdc736db332e2733be7 IPCC_AR6_WGI_Figure_3_36.png]]

Figure 3.36 | '''Life cycle of (left) El Niño and (right) La Niña events in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red).''' An event is detected when the December ENSO index value in year zero exceeds 0.75 times its standard deviation for 1951–2010. '''(a, b)''' Composites of the ENSO index (°C). The horizontal axis represents month relative to the reference December (the grey vertical bar), with numbers in parentheses indicating relative years. Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. '''(c, d)''' Mean durations (months) of El Niño and La Niña events defined as number of months in individual events for which the ENSO index exceeds 0.5 times its December standard deviation. Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of each panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. The ENSO index is defined as the SST anomaly averaged over the Niño 3.4 region (5°S–5°N, 170°W–120°W). All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

ENSO events are often synchronized to the seasonal cycle in the observations, as the associated SST anomalies tend to peak in boreal winter (November to January) and be at their weakest in the boreal spring (March to April) ( [[#Harrison--1998|Harrison and Larkin, 1998]] ; [[#Larkin--2002|Larkin and Harrison, 2002]] ). The majority of CMIP5 and CMIP6 models broadly reproduce the seasonality of ENSO SST variability in the central equatorial Pacific ( [[#Taschetto--2014|Taschetto et al., 2014]] ; [[#Abellán--2017|Abellán et al., 2017]] ; [[#Grose--2020|Grose et al., 2020]] ; [[#Planton--2021|Planton et al., 2021]] ) (Figure 3.37). However, CMIP5 models, while displaying an improvement on CMIP3 models, appear to under-represent the magnitude of the seasonal variance modulation ( [[#Bellenger--2014|Bellenger et al., 2014]] ). This under-representation of seasonal variance modulation continues in CMIP6 models, which display no statistically significant difference in this behaviour when compared to CMIP5 models ( [[#Planton--2021|Planton et al., 2021]] ) (Figure 3.37).

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[[File:b6358ab26bb5a3f75228d78f8ba31f08 IPCC_AR6_WGI_Figure_3_37.png]]

Figure 3.37 | '''ENSO seasonality in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red) for''' '''1951–2010''' '''. (a)''' Climatological standard deviation of the monthly ENSO index (SST anomaly averaged over the Niño 3.4 region; °C). Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. '''(b)''' Seasonality metric, which is defined for each model and each ensemble member as the ratio of the ENSO index climatological standard deviation in November–January (NDJ) to that in March–May (MAM). Each dot represents an ensemble member from the model indicated on the vertical axis. The boxes and whiskers represent the multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6 individually. The CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of the panel. The multi-model ensemble means and percentile values are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes. All results are based on five-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Observations show strong multi-decadal modulation of ENSO variance throughout the 20th century, with the most recent period displaying larger variability while the mid-century displayed relatively low ENSO variability (Figure 2.36; [[#Li--2013|Li et al., 2013]] ; [[#McGregor--2013|McGregor et al., 2013]] ; [[#Hope--2017|Hope et al., 2017]] ). As assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] , ENSO amplitude since 1950 is higher than over the pre-industrial period from 1850 as far back as 1400 ( ''medium confidence'' ), but there is ''low confidence'' that it is higher than the variability over periods prior to 1400. This reported variance increase suggests that external forcing plays a role in the ENSO variance changes ( [[#Hope--2017|Hope et al., 2017]] ). However, large ensembles of single model or multiple model simulations do not find strong trends in ENSO variability over the historical period, suggesting that external forcing has not yet modulated ENSO variability with a magnitude that exceeds the range of internal variability ( [[#Hope--2017|Hope et al., 2017]] ; [[#Maher--2018b|Maher et al., 2018b]] ; [[#Stevenson--2019|Stevenson et al., 2019]] ). This is consistent with the [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assessment that there is no clear evidence for a recent sustained shift in ENSO beyond the range of variability on decadal to millennial time scales ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ). CMIP5 and CMIP6 models show a decrease in ENSO variance in the mid-Holocene ( [[#Brown--2020|Brown et al., 2020]] ), though not to the extent seen in paleo-proxy records ( [[#Emile-Geay--2016|Emile-Geay et al., 2016]] ). This suggests that both modelled and observed ENSO respond to changes in external forcing, but not necessarily in the same manner.

Most CMIP5 and CMIP6 models are found to represent the general structure of observed SST anomalies during ENSO events well ( [[#Kim--2012|Kim and Yu, 2012]] ; [[#Taschetto--2014|Taschetto et al., 2014]] ; [[#Brown--2020|Brown et al., 2020]] ; [[#Grose--2020|Grose et al., 2020]] ). However, the majority of CMIP5 models display SST anomalies that: i) extend too far to the west ( [[#Taschetto--2014|Taschetto et al., 2014]] ; [[#Capotondi--2015|Capotondi et al., 2015]] ); and ii) have meridional widths that are too narrow ( [[#Zhang--2012|Zhang and Jin, 2012]] ) compared to the observations. CMIP6 models display a statistically significant improvement in the longitudinal representation of ENSO SST anomalies relative to CMIP5 models ( [[#Planton--2021|Planton et al., 2021]] ), however, systematic biases in the zonal extent and meridional width remain in CMIP6 models ( [[#Fasullo--2020|Fasullo et al., 2020]] ; [[#Planton--2021|Planton et al., 2021]] ). The ENSO phase asymmetry, where observed strong El Niño events are larger and have a shorter duration than strong La Niña events ( [[#Ohba--2009|Ohba and Ueda, 2009]] ; [[#Frauen--2010|Frauen and Dommenget, 2010]] ), is also under-represented in both CMIP5 and CMIP6 models ( [[#Zhang--2014|Zhang and Sun, 2014]] ; [[#Planton--2021|Planton et al., 2021]] ). In this instance, both CMIP5 and CMIP6 models typically display El Niño events that have a longer duration than those observed, La Niña events that have a similar duration to those observed, and there is very little asymmetry in the duration of El Niño and La Niña phases (Figure 3.36). [[#Roberts--2018|Roberts et al. (2018)]] find an improvement in amplitude asymmetry in a HighResMIP model, but the under-representation remains.

The continuum of El Niño events are typically stratified into two types (often termed ‘flavours’), Central Pacific and East Pacific, where the name denotes the location of the events’ largest SST anomalies (Annex IV.2.3; [[#Capotondi--2015|Capotondi et al., 2015]] ). As discussed in [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] , the different types of events tend to produce distinct teleconnections and climatic impacts (e.g., [[#Taschetto--2020|Taschetto et al., 2020]] ). The characteristics of El Niño events of these two flavours in CMIP5 were generally comparable to the observations ( [[#Taschetto--2014|Taschetto et al., 2014]] ). CMIP6 models, however, display a statistically significant improvement in the representation of this ENSO event-to-event SST anomaly diversity when compared with CMIP5 models ( [[#Planton--2021|Planton et al., 2021]] ). In addition to this ENSO event diversity, the short observational record also displays an increase in the number of the Central Pacific-type events in recent decades ( [[#Ashok--2007|Ashok et al., 2007]] ; [[#McPhaden--2011|McPhaden et al., 2011]] ), which has also been identified as unusual in the context of the last 500–800 years based on recent paleo-climatic reconstructions ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ; Y. [[#Liu--2017|]] [[#Liu--2017|Liu et al., 2017]] ; [[#Freund--2019|Freund et al., 2019]] ). However, the short observational record combined with observational ( [[#L’Heureux--2013|L’Heureux et al., 2013]] ) and paleo-climatic reconstruction uncertainties preclude firm conclusions being made about the long-term changes in the occurrence of different El Niño event types. Initial analysis with a selected number of CMIP3 models suggested that there may be a forced component to this recent prominence of Central Pacific-type events ( [[#Yeh--2009|Yeh et al., 2009]] ), but analysis since then suggests that this behaviour is (i) consistent with that expected from internal variability ( [[#Newman--2011|Newman et al., 2011]] ); and (ii) not apparent across the full CMIP5 ensemble of historical simulations ( [[#Taschetto--2014|Taschetto et al., 2014]] ). Analysis of single-model large ensembles suggests that changes to ENSO event type in response to historical radiative forcing are not significant (e.g., [[#Stevenson--2019|Stevenson et al., 2019]] ). These same results, however, also suggest that multiple forcings can have significant influences on ENSO type and that the net response will depend on the accurate representation of the balance of these forcings ( [[#Stevenson--2019|Stevenson et al., 2019]] ).

The climatic effects of ENSO outside the tropical Pacific largely arise through atmospheric teleconnections that are induced by ENSO-driven changes in deep tropical atmospheric convection and heating ( [[#Yeh--2018|Yeh et al., 2018]] ). The teleconnections to higher latitudes are forced by waves that propagate into the extratropics ( [[#Hoskins--1981|Hoskins and Karoly, 1981]] ) and respectively excite the Pacific-North American pattern ( [[#Horel--1981|Horel and Wallace, 1981]] ) and Pacific-South American pattern ( [[#Karoly--1989|Karoly, 1989]] ; [[#Irving--2016|Irving and Simmonds, 2016]] ) in the Northern and Southern Hemispheres. Given the influence of these teleconnections on climate and extremes around the globe, it is important to understand how well they are reproduced in CMIP models. What has also become clear is that spatial correlations of ENSO’s teleconnections calculated over relatively short periods (&lt;100 years) may not be the most effective way to assess these relationships ( [[#Langenbrunner--2013|Langenbrunner and Neelin, 2013]] ; [[#Perry--2020|Perry et al., 2020]] ). This is because the spatial patterns are significantly affected by internal atmospheric variability on relatively short time scales (&lt;100 years; [[#Batehup--2015|Batehup et al., 2015]] ; [[#Perry--2020|Perry et al., 2020]] ). However, looking at simplified metrics like the agreement in the sign of the teleconnections ( [[#Langenbrunner--2013|Langenbrunner and Neelin, 2013]] ), regional average teleconnection strength over land ( [[#Perry--2020|Perry et al., 2020]] ), or a combination of both ( [[#Power--2018|Power and Delage, 2018]] ) provides a more robust depiction of the teleconnection representation. Examining sign agreement for the teleconnection patterns, ensembles of CMIP5 AMIP simulations display broad spatial regions with high sign agreement with the observations, suggesting that the model ensemble is producing useful information regarding the teleconnected precipitation signal ( [[#Langenbrunner--2013|Langenbrunner and Neelin, 2013]] ). Looking at regional averages of CMIP5 historical simulations, [[#Power--2018|Power and Delage (2018)]] show that the average coupled model teleconnection pattern reproduces the sign of the observed teleconnections in the majority of the 25 regions analysed. The sign agreement between the observed teleconnection and the multi-model mean teleconnection remains strong in CMIP6 (18 out of 20 displayed regions; Figure 3.38), and the observed DJF (December–January–February) teleconnection strength falls within the modelled range in all of the displayed regions for temperature and precipitation. Note, however, that while there is broad agreement in ENSO teleconnections between CMIP6 models and observations during DJF (e.g., [[#Fasullo--2020|Fasullo et al., 2020]] ), there are regions and seasons where the modelled teleconnection strength is outside the observed range ( [[#Chen--2020|Chen et al., 2020]] ).

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[[File:101ddaa26d08600548b1cfc9dade867d IPCC_AR6_WGI_Figure_3_38.png]]

Figure 3.38 | '''Model evaluation of ENSO teleconnection for near surface air temperature and precipitation in boreal winter (December–January–February).''' Teleconnections are identified by linear regression with the Niño 3.4 SST index based on Extended Reconstructed Sea Surface Temperature (ERSST) version 5 over the period 1958–2014. Maps show observed patterns for temperature from the Berkeley Earth dataset over land and from ERSST version 5 over ocean (°C, '''top''' ) and for precipitation from GPCC over land (shading, mm day <sup>–</sup> <sup>1</sup> ) and GPCP worldwide (contours, period: 1979–2014). Distributions of regression coefficients (grey histograms) are provided for a subset of AR6 reference regions defined in Atlas.1.3 for temperature '''(top)''' and precipitation '''(bottom)''' . All fields are linearly detrended prior to computation. Multi-model multi-member ensemble means are indicated by thick vertical black lines. Blue vertical lines show three observational estimates of temperature, based on Berkeley Earth, GISTEMP and CRUTS datasets, and two observational estimates of precipitation, based on GPCC and CRUTS datasets. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Most CMIP5 and CMIP6 models exhibit ENSO behaviour during the historical period that, to first order, is qualitatively similar to that of the observed ENSO. Many studies are now delving deeper into the models to understand if they are accurately producing the dynamics driving ENSO and its initiation ( [[#Jin--2006|Jin et al., 2006]] ; [[#Bellenger--2014|Bellenger et al., 2014]] ; [[#Vijayeta--2018|Vijayeta and Dommenget, 2018]] ; [[#Bayr--2019|Bayr et al., 2019]] ; [[#Planton--2021|Planton et al., 2021]] ). For both CMIP3 and CMIP5, diagnostics of ENSO event growth appear to show that the models, while producing ENSO variability that is qualitatively similar to that observed, do not represent the balance of the underlying dynamics well. The atmospheric Bjerknes feedback is too weak in the majority of models, while the surface heat flux feedback is also too weak in the majority of models. The former restricts event growth, while the latter restricts event damping, which when combined allow most models to produce variability in a range that is consistent with the observations ( [[#Bellenger--2014|Bellenger et al., 2014]] ; S.T. [[#Kim--2014|]] [[#Kim--2014|Kim et al., 2014]] ; [[#Vijayeta--2018|Vijayeta and Dommenget, 2018]] ; [[#Bayr--2019|Bayr et al., 2019]] ). Analysis of ENSO representation in a subset of CMIP6 models by [[#Planton--2021|Planton et al. (2021)]] suggests that these issues remain.

To conclude, ENSO representation in CMIP5 models displayed a significant improvement from the representation of ENSO variability in CMIP3 models, which displayed much more intermodel spread in standard deviation, and stronger biennial periodicity ( [[#Guilyardi--2012|Guilyardi et al., 2012]] ; [[#Flato--2013|Flato et al., 2013]] ). In general, there has been no large step change in the representation of ENSO between CMIP5 and CMIP6, however, CMIP6 models appear to better represent some key ENSO characteristics (e.g., [[#Brown--2020|Brown et al., 2020]] ; [[#Planton--2021|Planton et al., 2021]] ). The instrumental record and paleo-proxy evidence through the Holocene all suggest that ENSO can display considerable modulations in amplitude, pattern and period (see also ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ). For the period since 1850, there is no clear evidence for a sustained shift in ENSO index beyond the range of internal variability. However, paleo-proxy evidence indicates with ''medium confidence'' that ENSO variability since 1950 is greater than at any time between 1400 and 1850 ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.2|Section 2.4.2]] ). Coupled models display large changes of ENSO behaviour in the absence of external forcing changes, and little-to-no variance sensitivity to historical anthropogenic forcing. Thus, there is ''low confidence'' that anthropogenic forcing has led to the changes of ENSO variability inferred from paleo-proxy evidence.

[[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] reports ''low confidence'' that the apparent change from East Pacific- to Central Pacific-type El Niño events that occurred in the last 20–30 years was representative of a long term change. While some climate models do suggest external forcing may affect the El Niño event type, most climate models suggest that what has been observed is well within the range of natural variability. Thus, there is ''low confidence'' that anthropogenic forcing has had an influence on the observed changes in El Niño event type.

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