Strong Eastern Pacific El Niño events determine ENSO amplitude and asymmetry

doi:10.21203/rs.3.rs-2699572/v1

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Strong Eastern Pacific El Niño events determine ENSO amplitude and asymmetry

https://doi.org/10.21203/rs.3.rs-2699572/v1

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The El Niño/Southern Oscillation (ENSO) varied considerably over the last 140 years and the reason of that is under debate. The warm phase of ENSO is termed El Niño, the cold phase La Niña. Here we show that the difference between periods of high and low ENSO variability results mainly from the amplitude of Eastern Pacific (EP) El Niños, while the amplitudes of Central Pacific (CP) El Niños and La Niñas are comparable. Further, ENSO asymmetry and amplitude covary, suggesting that the number of strong EP El Niños dominates both. We find similar relations in the 40 historical runs of the Large Ensemble from the CESM1-CAM5-BGC model. Finally, the fraction of strong EP El Niños explains the spread in ENSO amplitude and asymmetry in preindustrial control simulations from the CMIP6 database. This study emphasizes the need of a better understanding of the deterministic and stochastic factors that are important for the development of strong EP El Niño events in observations and climate models.

Earth and environmental sciences/Climate sciences/Atmospheric science

Earth and environmental sciences/Climate sciences/Ocean sciences

El Niño/Southern Oscillation (ENSO) in the tropical Pacific is the globally dominant climate mode on interannual time scales, with environmental and socio-economic impacts in the Pacific region and beyond (McPhaden 1999; McPhaden et al. 2020). El Niño is associated with warmer than normal sea surface temperature (SST) in the eastern equatorial Pacific (EEP) and central equatorial Pacific (CEP), which increases the deep convection over the CEP and weakens the zonal surface winds over the western equatorial Pacific (WEP) (Philander 1990). This in turn leads to a reduction of the thermocline slope in the equatorial Pacific, with a deeper thermocline in the CEP and EEP reinforcing the SST warming there (Bjerknes 1969). During La Niña the situation is mostly opposite to El Niño, but with considerable asymmetries: SST anomalies (SSTa) tend to be weaker and located further to the west during La Niña than during El Niño (Takahashi et al. 2011; Dommenget et al. 2013; Capotondi et al. 2014; Timmermann et al. 2018). Further, El Niño events are separated into central Pacific (CP) and eastern Pacific (EP) events, where the latter tend to have larger amplitudes than the former (Capotondi et al. 2014; Timmermann et al. 2018).

Instrumental SST over the tropical Pacific is available since the late 19th century and ENSO amplitude varied substantially over this period (Fig. 1) (Bunge and Clarke 2009; Okumura et al. 2017). The Pacific Decadal Oscillation (PDO) is one possible factor influencing ENSO. For example, the EEP SST warmed after 1977 and cooled after 2000 (Mantua and Hare 2002; England et al. 2014), concurrent with the phase switch in the PDO (Power et al. 2021). The switch in the PDO phase may explain the change in ENSO (Sun and Yu 2009; Hu et al. 2013; Lübbecke and McPhaden 2014; Timmermann et al. 2018; Fedorov et al. 2020). However, there is an ongoing debate about cause and effect. For example, is a larger ENSO amplitude caused by a warmer EEP or a warmer EEP the result of larger ENSO amplitude (Power et al. 2021). Further, it is also unclear to which extent the recent changes in ENSO characteristics are due to internal variability or anthropogenic climate change (Cai et al. 2021).

There is a long standing discussion on how ENSO amplitude will change under global warming (Meehl et al. 2007; Latif and Keenlyside 2009; Collins et al. 2010; Bellenger et al. 2014; Zheng et al. 2016; Beobide Arsuaga et al. 2021). An important factor for this uncertainty is the biased ENSO dynamics in many CMIP models (Guilyardi et al. 2009; Bellenger et al. 2014; Bayr et al. 2019; Beobide Arsuaga et al. 2021; Planton et al. 2021). The bias can partly be explained by the equatorial Pacific cold SST bias that leads to an error compensation of the atmospheric feedbacks (Kim et al. 2014; Bayr et al. 2018, 2019, 2020). In the recent years, more consensus in ENSO projection could be gained by focusing on those climate models that simulate important ENSO properties more realistically (Cai et al. 2020b, 2021). For example, all CMIP6 models with a realistic ENSO asymmetry predict an increase in ENSO amplitude over the EEP (Cai et al. 2021).

Here, we wish to understand the causes of ENSO-amplitude changes under present day conditions, a prerequisite to reliably projecting future ENSO variability. We analyze ENSO-amplitude variability in observations over the period 1880–2019. Due to the limited observations, we additionally support our findings with 40 historical runs of the CESM1 Large Ensemble and preindustrial control (pictl) integrations of CMIP6 models.

ENSO amplitude and asymmetry in observations

ENSO amplitude varied considerably over the last 140 years as seen in the SSTa over the CEP (Niño3.4, see Methods, Fig. 1a), highlighted by its 20-year running standard deviation (Fig. 1b) and the sixth order polynomial fit. There is a high ENSO amplitude at the end of the 19th and beginning of the 20th century, a decrease until 1940 and an increase in the 1990s (Fig. 1b). This time evolution is in general consistent among different SST datasets, although they disagree on the exact ENSO amplitude before the satellite era.

ENSO asymmetry is shown in a composite analysis using the Niño3.4 index as selection criterion. On average El Niño is stronger and located further to the east than La Niña (Fig. 2a,b), which is highlighted by the amplitude and longitude of the Center of Heat Index (CHI, see Methods, Giese and Ray, 2011), amounting to + 1.3 K, 129°W for El Niño and − 1.0 K, 140°W for La Niña. The difference between the patterns (Fig. 2c) shows a positive pole in the east close to the South American coast and a negative horseshoe-like pattern in the west. ENSO asymmetry is measured by the difference between this eastern and western pole (index boxes in Fig. 2c, see Methods), as suggested by Dommenget et al. (2013).

The composite patterns shown in Fig. 2a,b) are the average over individual ENSO events that are quite diverse in terms of center and amplitude (Capotondi et al. 2014; Timmermann et al. 2018). To highlight the diversity of the ENSO events we show the amplitude and longitude of the CHI for each El Niño and La Niña month in the observational period (Fig. 2d). Most noticeably, during El Niño the CHI forms a horn-shaped pattern with the highest amplitudes around 120°W, while this is much less pronounced during La Niña. The distribution for El Niño is related to the larger amplitudes of EP El Niños in comparison to CP El Niños (see Methods), with largest amplitudes during the EP El Niños of 1982/1983 and 1997/1998 (red triangles and stars in Fig. 2d). The strong EP El Niño of 2015/2016 (red squares) had its center around 128°W and a weaker amplitude than the extreme EP El Niño of 1997/1998. Even though the 2015/2016 El Niño was comparable to the 1997-98 El Niño in terms of the Niño3.4 SSTa, the Intertropical Convergence Zone (ITCZ) did not migrate to the equator, which may explain the in total weaker amplitude and more western center. Therefore we define strong EP El Niños as events with Niño3 (see Methods) SSTa exceeding 1.5 K and extreme EP El Niños as events that additionally exhibited precipitation in excess of more than 5 mm/day in the Niño3 region as during the El Niños of 1982/1983 and 1997/1998 (Cai et al. 2014a). The category strong EP El Niños includes the extreme EP El Niños, if not explicitly mentioned otherwise.

The amplitude and center are more evenly distributed during La Niña, with only a slight maximum around 145°W. This is why we do not distinguish between CP and EP La Niñas. The shape of the CHI (Fig. 2d) is very similar in the four observational datasets (see Extended Data Fig. A1).

In the period with low ENSO amplitude (LOW: 1910–1969, blue symbols in Fig. 2d), El Niño events have similar amplitudes for centers over the CEP as during the periods with high ENSO amplitude (HIGH: 1880–1909 & 1970–2019, red symbols), but the strong EP El Niño events are missing. The horn-shaped pattern of El Niño is clearly visible in HIGH while it is much less pronounced in LOW, as HIGH includes the strong EP El Niño events of 1888/1889, 1896/1897, 1902/1903, 1972/1973, 1982/1983, 1997/1998 and 2015/2016. In LOW, only one strong EP El Niño was observed in 1930/1931. For La Niña, the HIGH periods exhibit slightly larger amplitudes around 145°W than the LOW period, but the difference is much less pronounced as for El Niño (blue and red lines in Fig. 2d). This indicates that CP El Niños can only grow to a certain amplitude, while EP El Niños can attain much larger amplitudes. There is not such a clear relation between center and amplitude for La Niña. Taken together, this explains the significant covariability of ENSO amplitude and asymmetry (Fig. A2a), as the number of strong EP El Niño events appears to determine these relations. A bootstrapping test with 10,000 randomly picked 50 years of SSTa underlines these results (Fig. A3i-l and Methods). This is further supported by the ENSO amplitude, the total asymmetry, the amplitude asymmetry, the spatial asymmetry and the fraction of strong EP El Niño events being all higher in HIGH than in LOW (Table 1).

In summary, the observations indicate a significant relation between ENSO amplitude and ENSO asymmetry in the last 140 years, where the number of strong EP El Niños is the defining factor. This relation, however, must be considered with some caution due to the short observational record and large uncertainties in the pre-satellite era.

ENSO amplitude and asymmetry in CESM-LE

Therefore, we investigate in the following the CESM1 Large Ensemble (CESM-LE) to obtain more insight into the relation between ENSO amplitude and asymmetry and strong EP El Niño events. Differences between the 40 simulations of the CESM-LE are solely due to internal variability, as the external forcing is identical. We analyze here the detrended anomalies of the period of 1920–1969 in which the external forcing is relatively weak. The ENSO amplitude ranges between 0.69 K and 1.18 K amongst the simulations (Fig. 3a). The CESM-LE simulates the ENSO asymmetry realistically (0.76 K) (Fig. 2f-h), but with a total asymmetry higher than in observations (0.54 K). The amplitude asymmetry and spatial asymmetry (see Methods) are slightly smaller than in observation (0.2 K and 9°, respectively, compared to 0.3 K and 11°). As the center of the El Niño and La Niña pattern are both shifted by about 10° to the west relative to the observations, we use here boxes for the total asymmetry calculation that are further west (125°E-175°E, 5°S-5°N and 80°W-175°W, 5°S-5°N). The ENSO asymmetry varies substantially in the CESM-LE (Fig. A2b), with a regression value with ENSO amplitude (2.2 K/K) similar to that observed and a correlation value (0.82) larger than in observations (1.9 K/K and 0.59, respectively).

To investigate the causes of the differences in ENSO amplitude among the individual simulations, we define a HIGH sub-ensemble as the 10 members with the largest ENSO amplitude (average ENSO amplitude 1.08 K) and a LOW sub-ensemble as the 10 members with the smallest ENSO amplitude (average ENSO amplitude 0.78 K). The CESM-LE simulates the horn-shaped relation between longitude and amplitude of the CHI of SSTa for El Niño and like in observations, this relation is much less pronounced for La Niña (Fig. 2e). In particular, there are considerably more strong and extreme EP El Niños in HIGH than LOW (squares and stars in Fig. 2e, respectively). For La Niña, the largest amplitudes in HIGH are located around 160°W with many of the strong events occurring in the year after strong or extreme EP El Niños (squares and stars, respectively). In general, the larger amplitudes are more broadly distributed over all longitudes for La Niña than El Niño.

We find a higher percentage of strong EP El Niños in runs with higher ENSO amplitude (r = 0.80, Fig. 3a), with up to 35% of all El Niño months stemming from strong EP El Niños. Further, there is a strong link between ENSO amplitude and the center of the El Niño pattern (Fig. 3b), while the center of La Niña shows no correlation with ENSO amplitude (not shown). This demonstrates the crucial role of the strong EP El Niños for the larger ENSO amplitude. Strong EP El Niños are associated with more precipitation in DJF in the Niño3 region (Fig. 3c), because the weaker meridional SST gradient allows the ITCZ to migrate southward to the equator (Cai et al. 2014b) (Fig. 3d). Further, we find relations of similar strength when replacing ENSO amplitude by ENSO asymmetry (Fig. 3e-h). The ENSO amplitude, total asymmetry, amplitude asymmetry, spatial asymmetry and the fraction of strong EP El Niño events derived from the CESM-LE support the hypothesis that the strong EP El Niños dominate the larger ENSO amplitude and asymmetries in HIGH, all being larger in HIGH than LOW (Table 1).

Finally, the runs in HIGH feature a weak El Niño-like mean state, with a warmer EEP and colder WEP than the runs in LOW (Fig. 4a), i.e., a weaker zonal SST gradient, consistent with Zheng et al. (2018) and Cai et al. (2020a) also analyzing the CESM-LE. This mean state difference exhibits a similar east-west dipole as the asymmetry pattern (Fig. 2h) that are highly correlated with 0.77. The El Niño-like mean state encompasses the coupled ocean-atmosphere system of the tropical Pacific, with strong correlations between the individual members (Fig. 4). A similar El Niño-like mean state can also be found in the observations/reanalysis data, when applying a bootstrapping approach (Fig. A3a-h). The relation between ENSO amplitude and mean state appears to be robust across datasets and may indicate an self-modulating effect of ENSO (Cai et al. 2020a), as the warmer EEP may explain the higher fraction of strong EP El Niños due to a lower convective threshold.

ENSO amplitude and asymmetry in CMIP6 models

We investigate the relation between strong EP El Niños and ENSO amplitude and asymmetry in 500-year long preindustrial control (pictl) integrations of the CMIP6 models and for comparison in the CESM-control (ctl) run. The CMIP6 ensemble exhibits a large spread in ENSO amplitude ranging from 0.43 K to 1.53 K (Fig. 5a). Further, half of the models strongly underestimate the ENSO asymmetry with an asymmetry of less than half of the observed. We define the more realistic models as the ones that have more than half of the observed asymmetry and an ENSO amplitude of less than 1.2 K (black symbols in Fig. 5a). The CHI of all individual models are shown in Fig. A4.

There is a highly significant positive correlation of 0.82 between the ENSO amplitude and asymmetry, i.e. models with a larger ENSO amplitude also tend to simulate a larger asymmetry. The fraction of strong EP El Niño events varies between 0.00 and 0.50 and is strongly correlated with the ENSO amplitude (r = 0.88, Fig. 5b) and with the ENSO asymmetry (r = 0.84, Fig. 5c). Thus, the number of strong EP El Niño events in comparison to all El Niño events explains the largest part of the ensemble spread in ENSO amplitude and asymmetry. The spread in strong EP El Niño events between the models can partly be explained by the cold SST bias (Fig. 5e), that causes a too weak zonal wind feedback (Fig. 5d) and biased ENSO dynamics (e.g. Guilyardi et al. 2009, 2020; Bayr et al. 2019, 2021).

This study investigates the influence of strong EP El Niño events on ENSO amplitude and ENSO asymmetry by means of observations and climate models. We find that in observations, ENSO amplitude and ENSO asymmetry are governed by strong EP El Niño events. Due to the limited observations, we additionally investigated the CESM-LE that strongly supports the observational findings. CMIP6 models are also analyzed and a large part of the inter-model spread in ENSO amplitude and ENSO asymmetry can be explained by the fraction of strong EP El Niños.

About half of the CMIP6 models underestimate the ENSO asymmetry considerably, reflecting the problems of the models in simulating strong EP El Niño events. This is a common problem in current climate models (Guilyardi et al. 2009, 2020), which can be partly explained by the cold equatorial SST bias that shifts the rising branch of the Walker Circulation to the west impairing the atmospheric feedbacks and thus a proper simulation of the ENSO dynamics (Bellenger et al. 2014; Bayr et al. 2019, 2021). In contrast, the CESM belongs to those models with realistic ENSO amplitude, asymmetry and fraction of strong EP El Niño events. The CESM-LE supports the observational finding that strong EP El Niño events determine ENSO amplitude and ENSO asymmetry.

A remaining question from the mean state difference in observation and the CESM-LE is if more strong EP El Niños are the consequence of an El Niño-like mean state or if an El Niño-like mean state is due to a self-modulating effect of the higher fraction of strong EP El Niño, or both. The strong EP El Niños need the warm mean-state SST in the EEP so that the ITCZ can migrate southward onto the equator. On the other hand, the El Niño-like mean state could be the result of the stronger ENSO asymmetry (Hayashi and Jin 2017; Cai et al. 2020a). The strong similarity between the ENSO asymmetry pattern and the mean state difference of HIGH and LOW ENSO amplitude runs in CESM-LE (pattern correlation = 0.77) could be an indication for a self-modulating effect of ENSO (Rodgers et al. 2004). However, there is an ongoing debate about cause and effect (e.g. Fedorov et al. 2020; Power et al. 2021).

This study highlights the fundamental role of strong EP El Niños for ENSO amplitude and asymmetry under present day conditions. Further investigations are needed to enhance the understanding of the deterministic and stochastic factors causing strong EP El Niño events and the role of strong EP El Niño events for ENSO amplitude under global warming.

Observations and reanalysis data

Observed SSTs for the period 1880–2019 are taken from HadISST (Rayner et al. 2003), ERSSTv5 (Huang et al. 2017), COBE-SSTv2 (Ishii et al. 2005) and Kaplan SST (Kaplan et al. 1998). We use the SST data from 1880 onwards, as before 1880 the data sets show large disagreement due to sparse observations in the tropical Pacific (Bunge and Clarke 2009). The atmospheric variables, i.e. zonal wind stress (TAUX) and precipitation (PR) are taken for the period 1959–2001 from ERA40 reanalysis (Uppala et al. 2005), for the period 1979–2017 from ERA-Interim reanalysis (Simmons et al. 2007) and for the period 1958–2019 from ERA5 reanalysis (Hersbach et al. 2020). The subsurface oceanic potential temperature (TEMP) is taken for the period 1959–2001 from ORAS3 (Balmaseda et al. 2008), for the period 1979–2017 from ORAS4 (Balmaseda et al. 2013) and for the period 1958–2019 from ORAS5 reanalysis (Zuo et al. 2019).

CESM1 Large Ensemble

We also analyze the period 1920–1969 in 40 runs of a large ensemble of the CESM1-CAM5-BGC model (hereafter named CESM-LE). All 40 runs start from the same timestep in the initial experiment (1st January 1920 from neutral ENSO conditions), that is changed by a tiny perturbation (10^–14°C) to generate different internal variability due to the chaotic behavior of the weather system also called butterfly effect (Lorenz 1969). The boundary conditions are identical in all runs, i.e., the CMIP5 historical forcing from 1920–1969. Additionally, we analyze a 500 years long preindustrial control experiment of the same model (named CESM-ctl).

CMIP6 data

From the CMIP6 database we use SST and precipitation of the preindustrial control (pictl) simulations from all available models that are at least 500 years long. All model data are interpolated on the same 2.5° x 2.5° regular grid. See Fig. 5 for a list of models.

Methods

General

The Niño3 region is defined as 90°W-150°W and 5°S-5°N, the Niño3.4 region as 120°W-170°W and 5°S-5°N, the Niño4 region as 160°E-150°W and 5°S-5°N and the Niño5 region as 130°E-160°E and 5°S-5°N. Monthly-mean values are used in the analyses. In the long datasets of observed SST and pictl runs, anomalies are calculated with respect to a 30-year running climatology. In all other datasets we use quadratically detrended anomalies from a fixed climatology for the whole time period. Using detrended anomalies from a fixed climatology for all datasets has only minor effects on the presented results.

Definition of ENSO amplitude and events

ENSO amplitude is measured by the standard deviation (stdv) of SST anomalies (SSTa) in the Niño3.4 region, as the SSTa in the Niño3.4 region represents both El Niño and La Niña events properly (Trenberth 1997). EP and CP El Niño events are defined here according to an adapted Trans-Niño-Index (TNI, Trenberth and Stepaniak 2001), measuring the normalized SSTA difference between eastern Pacific (120°W-80°W, 5°S-5°N) and the western Pacific (Niño4 region). Strong EP El Niño events are defined as EP El Niño months, in which the SSTa in Niño3 is larger than 1.5 K. Extreme EP El Niño events are strong EP El Niño events, that additionally have more than 5 mm/day PR in the Niño3 region (Cai et al. 2015). According to this definition e.g. the El Niño of 2015/2016 is a strong EP El Niño and the El Niños of 1982/1983 and 1997/1998 are extreme EP El Niños.

Definition of ENSO asymmetries

To reveal the asymmetry of ENSO we do a composite analysis of El Niño and La Niña events, with the SSTa in Niño3.4 > + 1.0 stdv as selection criterion for El Niño months and < -1.0 stdv for La Niña (Dommenget et al. 2013). A measure for the total asymmetry is then calculated as the difference between an eastern box (140°W-80°W, 5°S-5°N) and a western box (140°E-160°W, 5°S-5°N) from the difference pattern between El Niño and La Niña (Dommenget et al. 2013). Additional to the total asymmetry we also calculate a spatial asymmetry and an amplitude asymmetry utilizing the Center of Heat Index (CHI, Giese and Ray 2011) for the detection of the longitude and amplitude of the ENSO patterns: The CHI longitude is the amplitude-weighted center of mass of the SST pattern along the equator between 5°S and 5°N, i.e. the average over all amplitude-weighted longitudes that exceed 0.5 stdv of the Niño3.4 index. The CHI amplitude is the average amplitude over all grid points that exceed 0.5 stdv of the Niño3.4 index. Then, the spatial asymmetry is defined as the difference between the CHI longitude of the El Niño pattern minus the CHI longitude of the La Niña pattern. The amplitude asymmetry is defined as the difference between the CHI amplitude of the El Niño pattern minus the CHI amplitude of the La Niña pattern.

Bootstrapping approach for observations

As the observational record is quite short, we use a bootstrapping method to test the relation between ENSO amplitude, ENSO asymmetry and other variables. Therefore, we pick 10,000 times randomly 50 years of anomalies out of the in total available years and calculate ENSO amplitude, total asymmetry, spatial asymmetry, amplitude asymmetry and other measures from these years.

Acknowledgements

We acknowledge the Hadley Center, NOAA, the Japanese Oceanographic Data Center, the Columbia University and ECMWF for providing the observational and reanalysis datasets. Further we acknowledge NCAR for providing the CESM1 Large Ensemble and the World Climate Research Program’s Working Group on Coupled Modeling, the individual modeling groups of the Coupled Model Intercomparison Project (CMIP6). This work was supported by the Deutsche Forschungsgemeinschaft (DFG) project “Influence of Model Bias on ENSO Projections of the 21st Century” through grant 429334714.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Data analysis and the first draft of the manuscript was performed by Tobias Bayr. Joke Lübbecke and Mojib Latif commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability

The datasets analyzed during the study are available at:

CESM-LE: https://www.cesm.ucar.edu/community-projects/lens/data-sets

CMIP6: https://esgf-node.llnl.gov/projects/cmip6/

HadISST: https://www.metoffice.gov.uk/hadobs/hadisst/

ERSSTv5, COBE-SSTv2 and Kaplan-SST: https://psl.noaa.gov/data/gridded/tables/sst.html

ERA5: https://cds.climate.copernicus.eu/#!/search?text=ERA5%20monthly&type=dataset

ERA40, ERA-Interim: https://apps.ecmwf.int/datasets/

ORAS5: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-oras5?tab=form

ORAS3, ORAS4: https://www.cen.uni-hamburg.de/icdc/data/ocean.html

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Table 1

ENSO amplitude, total asymmetry, amplitude asymmetry, spatial asymmetry and fraction of strong EP El Niños in ALL, LOW and HIGH periods in Observations and ALL, LOW and High sub-ensembles of the CESM-LE.
	Observations			CESM-LE
	ALL	LOW	HIGH	ALL	LOW	HIGH
ENSO amplitude [K]	0.78	0.68	0.85	0.93	0.78	1.08
Total asymmetry [K]	0.53	0.26	0.78	0.76	0.49	1.12
Amplitude asymmetry [K]	0.23	0.09	0.32	0.24	0.16	0.38
Spatial asymmetry [°]	10.4	5.6	13.8	8.9	5.4	11.7
Fraction of strong EP El Niños [%]	21	7	31	24	17	33

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Strong Eastern Pacific El Niño events determine ENSO amplitude and asymmetry

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