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The Ensemble Oceanic Nino Index



We describe a new El Nino Southern Oscillation (ENSO) index that we call the Ensemble Oceanic NINO Index (Ensemble ONI). Ensemble ONI uses monthly sea surface temperature (SST) anomalies in the NINO3.4 region from 32 input SST datasets, which form the ensemble, and our new monthly ENSO index extends from the present back to the year 1850. We use the input datasets to quantify the uncertainty in the monthly index values. The uncertainty is calculated from datasets that are not completely independent from each other, but comparing our findings to other estimates of uncertainty in SST, well‐documented historical ENSO events, a detailed consideration of literature‐based definitions of ENSO events, and proxy‐based determinations of past ENSO events, we show that our uncertainty estimates from the ensemble of input datasets is comparable both in terms of average magnitude and time‐varying trend with other studies. Similar to past research, we find that ENSO events occur every 4‐5 years on average, and there have been six “Super” El Ninos (1877‐78, 1888‐89, 1972‐73, 1982‐83, 1997‐98, 2015‐16) that statistically rise above all other El Ninos since 1850. Finally, the time span of our work shows that El Nino events were most intense at ends of both the 19th Century and the 20th Century, with a lull in the mid‐1900s, corroborating previous instrumental, written, and proxy records.
The Ensemble Oceanic Niño Index
Eric J. Webb | Brian I. Magi
Department of Geography and Earth
Sciences, University of North Carolina at
Charlotte, Charlotte, North Carolina, USA
Brian I. Magi and Eric J. Webb,
Department of Geography and Earth
Sciences, University of North Carolina at
Charlotte, Charlotte, NC 28223, USA.
Email: (B.I.M.) and (E.J.W.)
We describe a new El NiñoSouthern Oscillation (ENSO) index that we call
the Ensemble Oceanic Niño Index (Ensemble ONI). Ensemble ONI uses
monthly sea surface temperature (SST) anomalies in the Niño3.4 region from
32 input SST datasets as the basis for the ensemble, and our new monthly
ENSO index extends from the present back to the year 1850. We use the input
datasets to quantify the uncertainty in the monthly index values. The uncer-
tainty is calculated from datasets that are not completely independent from
each other, but we use other estimates of uncertainty in SST, well-documented
historical ENSO events, a detailed consideration of literature-based definitions
of ENSO events, and proxy-based determinations of past ENSO events, to show
that our uncertainty estimate from the ensemble of input datasets is compara-
ble with other studies both in terms of average magnitude and the time-vary-
ing trend. Similar to past research, we find that ENSO events occur every 4
5 years on average, and there have been six SuperEl Niños (18771878,
18881889, 19721973, 19821983, 19971998, 20152016) that statistically rise
above all other El Niños since 1850. Finally, the time span of our work shows
that El Niño events were most intense at ends of both the 19th century and the
20th century, with a lull in the mid-1900s, corroborating previous instrumen-
tal, written, and proxy records.
centennial, climate, decadal, ENSO, ensembles, seasonal, statistical methods,
teleconnections (AO, NAO, MJO, ENSO, SSW, ONI, ADO, MJO)
El NiñoSouthern Oscillation (ENSO) is a mode of climate
variability that results from a coupled interaction between
the ocean and overlying atmosphere in the tropical Pacific
Ocean (Bjerknes, 1966; Enfield, 1989; Neelin et al., 1998;
McPhaden et al., 2020), and has remote teleconnections
(Bjerknes, 1969; Wallace and Gutzler, 1981; Dai and
Wigley, 2000) linked to equatorial Pacific sea surface tem-
perature (SST) anomalies. The development of ENSO
indices (e.g., Trenberth and Stepaniak, 2001; Wolter and
Timlin, 2011) and their refinements (e.g., van Oldenborgh
et al., 2021) have helped to characterize the state of the
tropical Pacific and quantitatively diagnose teleconnections
(Barnston et al., 1997), as well as being used to evaluate cli-
mate model output (Fredriksen et al., 2020; Wang
et al., 2021) and constrain proxy records (McGregor
et al., 2013).
SST anomalies in the Niño3.4 region (5S5N, 120
170W) have been found to statistically capture the state
Received: 21 September 2021 Revised: 31 December 2021 Accepted: 24 January 2022
DOI: 10.1002/joc.7535
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any
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© 2022 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.
Int J Climatol. 2022;121. 1
of ENSO and various ENSO teleconnections (Trenberth,
1997; Hanley et al., 2003; Giese and Ray, 2011), and this
motivated the creation of the Oceanic Niño Index (ONI) in
the 1990s by the National Oceanic and Atmospheric
Administration (NOAA) Climate Prediction Center (CPC)
(Barnston et al., 1997; Huang et al., 2017). NOAA ONI is
calculated using tri-monthly averaged SST anomalies from
the Extended Reconstructed Sea Surface Temperature Ver-
sion 5 (ERSSTv5; Huang et al., 2017) in the Niño3.4 region
with a 30-year sliding base period that is updated every
In this study, we present a new ENSO index that we
call the Ensemble Oceanic Niño Index (Ensemble ONI)
that builds on methods similar to NOAA ONI (Barnston
et al., 1997; Huang et al., 2017), but with significant
advances. First, we include an ensemble of 32 SST recon-
structions and reanalysis datasets, as opposed to relying
on a single SST dataset. A similar approach has been
applied to other ENSO data sources including proxies
(Braganza et al., 2009; McGregor et al., 2010), documen-
tary records (Quinn et al., 1987), and climate models
(e.g., Yeh et al., 2012; Fredriksen et al., 2020; Jiang
et al., 2021; Wang et al., 2021). However, fewer ensemble-
based analyses of ENSO exist for instrumental datasets
and reanalysis models (e.g., Zhu et al., 2012). Second,
while the NOAA ONI spans 1950 to present, Ensemble
ONI extends from 1850 to present. Third, we provide esti-
mates of the uncertainty in the monthly Ensemble ONI
values based on the spread across the input SST datasets,
which is both an advance compared to NOAA ONI and a
unique contribution to the suite of ENSO indices. Quanti-
fying uncertainty builds on evolving discussions of para-
metric and structural uncertainty estimation in SST at
the temporal scale of ENSO events (Huang et al., 2016a)
and at a range of spatial scales (Kennedy, 2014; Liu
et al., 2015; Huang et al., 2016b; Kennedy et al., 2019).
After presenting our methods and results, we compare
Ensemble ONI with other ENSO indices, detail and jus-
tify our uncertainty estimation, and then discuss the
results in terms of both magnitude and uncertainty rela-
tive to ENSO events since 1850.
The Ensemble ONI is an index expressed as an SST
anomaly averaged over the Niño3.4 region (5S5N,
170W) and is calculated using an ensemble of SST
datasets. The time-varying spread across input datasets is
the basis for our calculation of the confidence interval in
the monthly Ensemble ONI.
SST anomalies in the Niño3.4 region (5S5N,
120W170W) have been found to statistically capture
the state of ENSO and various ENSO teleconnections
(Trenberth, 1997; Hanley et al., 2003; Giese and
Ray, 2011). Furthermore, Figure 1a shows the empirical
orthogonal function (EOF) loading region of the mean
SST from COBE SST2 (Hirahara et al., 2014), COBE SST
(Ishii et al., 2005), ERSSTv4 (Huang et al., 2015),
ERSSTv5 (Huang et al., 2017), Kaplan V2 (Kaplan
et al., 1998), HadISST (Rayner et al., 2003), and HadISST
v2.1 (Titchner and Rayner, 2014) for the 18912010 time
span, a period during which all datasets overlap. The
Niño3.4 region is in the heart of the strongest correlation
with SST variability (Barnston et al., 1997), whereas other
Niño regions are more variable in their loading and tend
to capture other flavoursof ENSO (Trenberth and
Stepaniak, 2001; Ashok et al., 2007). Figure 1b depicts the
correlation between the Southern Oscillation Index (SOI;
Troup, 1965; Ropelewski and Jones, 1987) and detrended
Tropical Pacific SST anomalies over the same aforemen-
tioned period. The highest correlations between the SOI
and Tropical Pacific SST anomalies overlap over the
Niño3.4 region, which also suggests that the Niño3.4
region is most highly correlated with other independently
derived measures of ENSO, consistent with previous
work (e.g., Barnston et al., 1997).
Table 1 lists the 32 observational, reanalysis, and sat-
ellite datasets we used. The temporal ranges of individual
datasets vary, but we combine the SST data from 1850 to
present (June 2021, currently) to build the Ensemble
ONI. The monthly SST value is determined as the SST
averaged in Niño3.4 with a minimum of 30% spatial com-
pleteness for a validmonthly value to account for
datasets like HadSST4 (Kennedy et al., 2019) and ICO-
ADS R3.0 (Freeman et al., 2017) that are not spatially
complete. Input data reported as SST anomalies
(e.g., Kaplan Extended v2; Kaplan et al., 1998) are first
converted to SSTs using their respective climatologies
and then averaged for the Niño3.4 domain.
Similar to NOAA ONI (Barnston et al., 1997), we cal-
culate the Niño3.4 SST anomaly using 30-year climato-
logical base periods that are updated every 5 years. This
moving window helps minimize both the overall annual
and seasonal warming trends in the Niño3.4 region to
produce an ENSO index that better reflects variability in
ENSO state. The 30-year base periods are centred on the
years of the calculation and start in 1850, noting that the
present-day period from 20032021 uses a noncentred
30-year base period from 1990 to 2019 (Table S1,
Supporting Information).
The 30-year climatology is calculated as the monthly
median of the per-dataset Niño3.4 SST (values in
Table S2), where we require a minimum of 75% of the
30 years to be available for the dataset to contribute to
that 30-year climatological base period. We use the
monthly median (as opposed to the mean) to minimize
the influence of outliers in the per-dataset Niño3.4 SST
anomalies since (a) we do not attempt to evaluate the
quality of individual months of individual input data,
and (b) we aim to calculate a statistically robust measure
of the central tendency of Niño3.4 SST from the ensemble
to best characterize variability in ENSO state. The num-
ber of contributing datasets to each of the 30-year clima-
tologies varies from 7 to 30, generally increasing with
time. We calculate the time series of Niño3.4 SST anoma-
lies for each dataset using our 30-year base periods
applied to the 5-year period centred on the 30-year base
period (Table S1). We then calculate the median SST
anomaly across all available datasets for every month,
where the maximum number of datasets for a given
month is 32 (Table 1).
The Ensemble ONI is calculated by applying a
3-month moving average to the time series of Niño3.4
SST anomalies, centred on the reported month, per the
methodology of NOAA ONI (Barnston et al., 1997). For
uncertainty, we calculate the 3-month moving average of
the 68.27% statistical percentile range. This specific per-
centile range allows for a rough comparison with the
magnitude of the 1-sigma range derived from the calcula-
tion of standard deviation, but avoids the need for the
parametric assumption of a Gaussian distribution. We
argue below that using a percentile range is important in
our ensemble-of-datasets approach since both the outliers
and the number of datasets are time-varying. The distri-
bution in any given month is approximately Gaussian,
but each dataset (Table 1) produces its own SST value for
the Niño3.4 region and quality-control of individual
datasets is not the aim of this work.
Figure 2 shows the time series of Ensemble ONI, shaded
to show positive values in red and negative values in
blue. The overall distribution from Figure 2 is slightly
positively skewed (skewness =0.43) from a normal distri-
bution, with the minimum to maximum range being
FIGURE 1 Niño 3.4 region
170W, 5N5S) is denoted
by the white box along with
(a) leading tropical Pacific (100E
90W, 30N30S) SST EOF during
SeptemberFebruary (18912010)
using an ensemble mean field of the
Kaplan Extended SSTv2 datasets
(see Table 1). All datasets were bi-
linearly interpolated to a 5 ×5grid
prior to the construction of the SST
EOF, and (b) Pearson correlation
coefficient between detrended
tropical Pacific (100E90W, 30N
30S) SST anomalies from an
ensemble mean field of the
Kaplan Extended SSTv2 datasets
(see Table 1) and the Southern
Oscillation Index during
SeptemberFebruary (18912010)
[Colour figure can be viewed at]
2.14 to 2.81K and the median negative and positive
Ensemble ONI values being equal in magnitude at 0.44
and +0.44. Consistent with past research about ENSO
state and the dynamics of the Bjerknes feedback (An and
Jin, 2004; Okumura and Deser, 2010), our result shows
that the strongest El Niños are stronger than the stron-
gest La Niñas.
The Ensemble ONI reached a centennial-scale peak
in the late 1800s and again in the latter part of the record,
with a relatively quiet period from the early to middle
1900s (Figure 2), consistent with past studies (Garcia-
Herrera et al., 2008; Giese and Ray, 2011; Wolter and
Timlin, 2011; Ray and Giese, 2012; McGregor
et al., 2013). A 40-year moving average of Ensemble ONI
values greater than +1 (Figure 3) suggests that stronger
El Niño conditions are more frequent and have greater
amplitudes near the turn of the 20th and 21st centuries
than in the mid-20th century, with a period of cooler pos-
itive SST anomalies from about 1900 to 1960. This mul-
tidecadal to centennial scale nonstationary behaviour in
stronger El Niño conditions has been noted by other
studies (Enfield and Cid, 1991; Garcia-Herrera
TABLE 1 The 32 input datasets
used to calculate the ensemble ONI
Dataset Range Resolution Reference
HADISST2.1 18502010 1 ×1Titchner and Rayner (2014)
NOAA 20CRv3* 18502015 1 ×1Slivinski et al. (2019)
COBE SST2 18502018 1 ×1Hirahara et al. (2014)
HADSST4 18502020 5 ×5Kennedy et al. (2019)
NOAA 20CRv2c* 18512014 2 ×2Compo et al. (2011)
ERSSTv5 18542020 2 ×2Huang et al. (2017)
ERSSTv4 18542020 2 ×2Huang et al. (2015)
Kaplan Extended v2 18562020 5 ×5Kaplan et al. (1998)
HADISST 18702020 1 ×1Rayner et al. (2003)
NOAA 20CRv2* 18712012 2 ×2Compo et al. (2011)
SODAv2.2.4 18712010 0.5 ×0.5Giese and Ray (2011)
ICOADSv3 18782019 2 ×2Freeman et al. (2017)
COBE SST 18912020 1 ×1Ishii et al. (2005)
ERA-20C* 19002010 1 ×1Poli et al. (2016)
ERA-20CM* 19002010 1 ×1Hersbach et al. (2015)
CERA-20C* 19012010 1 ×1Laloyaux et al. (2018)
NCEP-R1* 19482020 2.5 ×2.5Kalnay et al. (1996)
HADSST2 19502012 5 ×5Rayner et al. (2006)
HADSST3 19502020 5 ×5Kennedy et al. (2011a)
ERSSTv3b 18802020 2 ×2Smith et al. (2008)
ORAS5* 19582018 0.25 ×0.25Zuo et al. (2019)
WHOI OA Flux v3* 19582019 2.5 ×2.5Yu et al. (2008)
CAC SST 19702003 2 ×2Reynolds (1988)
ERA-5* 19792019 0.28 ×0.28Hersbach et al. (2020)
NCEP CFSR* 19792015 0.5 ×0.5Saha et al. (2010)
NCEP DOE R2* 19792020 2 ×2Kanamitsu et al. (2002)
ERA-Interim* 19792019 1 ×1Dee et al. (2011)
NASA MERRA* 19792016 0.5 ×0.66Rienecker et al. (2011)
NASA MERRAv2* 19802016 0.5 ×0.625Gelaro et al. (2017)
OISSTv1 19812003 1 ×1Reynolds and Smith (1994)
OISSTv2.1 19812020 1 ×1Reynolds et al. (2002)
AQUA-MODIS 20022020 1 ×1Parkinson (2003)
Note: The 15 datasets listed with an asterisk are reanalysis or include an element of model/interpolation to
supplement observations.
et al., 2008). Negative SST anomalies (not shown), which
would correspond to months tending towards La Niña
conditions, do not exhibit any multidecadal behaviour
since the late 1800s.
Figure 4a shows Ensemble ONI bracketed by the
15.865 and 84.135% percentile values (Table S3), the dif-
ference of which corresponds numerically to the 68.27%
confidence interval (CI) to make this comparable, at least
in terms of magnitude, with the more typically reported
standard deviation. Specifically, we report the uncer-
tainty as half the difference of the 15.865 and 84.135%
percentile values, similar to the way standard deviation is
often reported as 1-sigma uncertainty. Standard devia-
tion, however, is better interpreted when the data is nor-
mally distributed with no influential outliers, whereas
using the median and percentile range implies no para-
metric assumptions and is far less sensitive to outliers.
Another advantage of using the percentile range as the
basis for uncertainty estimation is that the time-varying
asymmetry relative to the central estimate is captured,
and this quantitatively reveals how the input datasets
(Table 1) themselves sometimes lean towards more posi-
tive or negative SST anomalies (Figure S1). Similar to
Ensemble ONI, the uncertainty is reported as a moving
3-month average.
Figure 4b shows the time series of the uncertainty in
Ensemble ONI, calculated as described above so that
values are numerically similar to the standard deviation,
and is determined by interdataset variability in SST in
the Niño3.4 region (Figure S1). Figure 4b also shows the
HadSST4 (Kennedy et al., 2019) total uncertainty in two
spatial domains: the Niño3.4 region and an extended
Niño3.4 region (defined as 10S10N, 80
170W). To
calculate the HadSST4 total uncertainty, we propagate
the gridbox-specific total uncertainty to the larger spatial
regions by standard propagation of errors, following guid-
ance in Kennedy et al. (2019). The so-called Extended
FIGURE 2 Monthly Ensemble ONI from January 1850 to May 2021 [Colour figure can be viewed at]
FIGURE 3 (a) 40-year moving average of Ensemble ONI
values greater than +1, with values scaled to equal zero in the first
40-year period to show the relative behaviour of stronger El Niños
and shading corresponding to the standard error among the
contributing months, and (b) the number (N) of times this occurs
within the 40-year periods [Colour figure can be viewed at]
Niño3.4 region increases the maximum number of 5lati-
tude by 5longitude gridboxes to 80, compared to 20 in
the Niño3.4 region, noting that only a subset of gridboxes
actually have data in any particular month. The 1850
2021 average number of gridboxes with data is 10 in the
Niño3.4 region and 47 in the Extended Niño3.4 region.
After about 1960, the data in both regions is nearly 100%
complete, but from 1850 to 1960, both regions are more
variable, ranging from 0 to 75% complete. We define the
Extended Niño3.4 region as a way to bound our assess-
ment of the magnitude of Ensemble ONI uncertainty, as
described in section 4.1.
Our method of estimating uncertainty arises strictly
from interdataset variability, which itself is a relatively
simple calculation, but is similar to an estimate structural
uncertainty due to variations across multiple estimations
of SST anomalies. The interdataset variability is a mean-
ingful way to gauge uncertainty (e.g., Thorne et al., 2005;
Parker, 2016; Kent et al., 2017) since every dataset pro-
duces its own history of SST anomalies in the Niño3.4
region and the variability reflects differences in
institution-specific analysis or input choices. Figure S1
shows how the time series of monthly Niño3.4 SST values
from input datasets tend to converge in the present day
but also reveals the relatively large spread in the 19th
century. Our method does not specifically capture para-
metric uncertainty (e.g., Liu et al., 2015) due to the vari-
ability arising from parameters associated with choices
we make in our calculations. However, we show below
that our calculation provides reasonable estimation of
overall uncertainty and argue that our method is a more
efficient way to calculate uncertainty than an iterative
exploration of the parameter space.
Consistent with HadSST4 uncertainty, the Ensemble
ONI uncertainty decreases with time (Figure 4b),
reflecting the increasing sampling density, quality, and
FIGURE 4 (a) Monthly ensemble ONI (black) with the 15.865 and 84.135% percentiles (pink-purple) where the total range is the 68.27%
(1-sigma) confidence interval, (b) uncertainty in ensemble ONI (purple) with total uncertainty from HadSST4 for the Niño3.4 region (5S
5N, 120
170W, light blue) and for the Extended Niño3.4 region (defined here as 10S10N, 80
170W, green), where the uncertainty
from HadSST4 is smoothed over a 12-month period and the gaps in the time series are data gaps for the region, and (c) number of input
datasets for Ensemble ONI per month [Colour figure can be viewed at]
consistency of SST observations (Freeman et al., 2017;
Kennedy et al., 2019), and to some degree, the number of
input datasets (Figure 4c). The trend in the uncertainty
reflects trends in the density and quality of the instru-
mental record (Kennedy et al., 2011a; Kennedy
et al., 2011b; Kent et al., 2013; Hirahara et al., 2014;
Kennedy, 2014; Karl et al., 2015; Hausfather et al., 2017;
O'Carroll et al., 2019), but also differences that are
embedded in the datasets themselves. For example, some
of our input datasets (Kaplan et al., 1998) used older ver-
sions of ICOADS (Woodruff et al., 1987; Woodruff
et al., 2011) and vary in the sources of observational data.
These features are part of any reanalysis or observational
dataset (e.g., Parker, 2016). We argue that our ensemble
approach to calculating ONI strengthens the interpreta-
tion of the central estimate by including an asymmetric
confidence interval (Figure 4a) that captures the time-
varying differences across input datasets.
While the general trend in uncertainty is downward
with time, three periods of uncertainty increasing with
time are evident in Figure 4b: Prior to the 187778 El
Niño, and during the first and second World Wars. The
increasing uncertainty before 18771878 and WWI is
largely explained by a lack of data sources and observa-
tions in Niño3.4 (Thompson et al., 2008; Giese
et al., 2010; Freeman et al., 2017; Carella et al., 2018). For
WWI, ships were deployed for war and often destroyed.
For WWII, the increasing uncertainty was due to both a
similar loss of data from ship loss and to changes in how
SST observations were made. Many ships switched from
insulated bucket to engine room intake (ERI) SST mea-
surements, which have a high bias relative to bucket
measurements (Kennedy et al., 2019). Additionally, sam-
pling uncertainty/bias may arise from changes in the
time of day most observations were collected (Figure S2)
due to safer wartime sampling conditions during daylight
as utilization of lights on-board at night to carry out more
traditional bucket measurements of SST could attract
enemy ships (Freeman et al., 2017; Kent et al., 2017). Fur-
thermore, how this rapid paradigm shift in SST observa-
tions was addressed varied amongst dataset curators and
contributes to the uncertainty in the Ensemble ONI dur-
ing this time. For example, older datasets, such as Kaplan
Extended SSTv2 (Kaplan et al., 1998) and HadISST
(Rayner et al., 2003) applied no bias correction for ERI,
whereas later iterations of ERSST (Huang et al., 2017)
bias-corrected ship observations against night-time
marine air temperatures (Kent et al., 2013) somewhat
analogous to Folland et al. (1984), and COBE SST2
(Hirahara et al., 2014) applied a global mean bias correc-
tion. We therefore assert that time-varying discrepancies
between SST datasets and reanalysis reflect real changes
in uncertainty over the instrumental record.
4.1 |Assessing uncertainty
We calculate the uncertainty in Ensemble ONI from
datasets that are not completely independent from each
other, so we discuss both the sensitivity of the uncer-
tainty to the datasets themselves as well as mostly or
completely independent methods to justify the magni-
tude and sensitivity of our uncertainty estimate. We
argue that the variability across the input datasets in the
Niño3.4 region (Figure S1) is a valuable way to calculate
uncertainty (Parker, 2016) by showing our uncertainty to
be both conservative and in line with the magnitude of
other ways that uncertainty in ENSO state can and has
been estimated.
We start by assessing the sensitivity of the uncertainty
to the inclusion of reanalysis and model datasets with
overlapping (non-independent) input from SST datasets.
The average uncertainty in Figure 4b is 0.29 K, but when
excluding 15 reanalysis/model datasets (datasets listed
with asterisks in Table 1), the uncertainty actually
decreases to 0.27 K (Figure 5). This counter-intuitive
result of more dispersion when reanalysis/model datasets
are included is consistent with discussions of the value of
intercomparisons across what would be seemingly simi-
lar reanalysis datasets (e.g., Thorne et al., 2005;
Parker, 2016) and suggests that reanalysis interdepen-
dencies on SST datasets do not lead to under-dispersion
of uncertainty in Ensemble ONI. A plausible explanation
for this seemingly contradictory result could be that tech-
niques applied to in situ SST data from different institu-
tions contribute to less diversity than using the same SST
dataset in different reanalysis models. Specifically,
reanalysis models may have SST output that has inter-
acted with other reanalysis model fields and may be
mathematically relaxed towards the in situ SST, and
there are differences in data assimilation methods,
model-specific parameterizations, spatiotemporal resolu-
tion, and ensemble size (e.g., Laloyaux et al., 2018). Fur-
thermore, in situ SST datasets reconstruct SSTs in the
late 19th and early-mid 20th century by assuming SST
variability is the same as during the modern part of the
record, whereas reanalyses-based SSTs during this same
period are less reliant on this assumption than they are
on aspects of the models themselves (e.g., Giese and
Ray, 2011).
Although we do not show the result, we found that clus-
tering datasets with similar progeny (e.g., interdependence
on a single SST dataset, such as ERA20C, ERA20CM,
NOAA20CRv3, CERA-20C, ORAS5, and ERA-5 all using
HadISSTv2) also resulted in less dispersion then consider-
ing the datasets independently, especially prior to the
early-mid 20th century, when the least is known about
instrumentally based ENSO variability and estimates of
uncertainty should be more conservative. This is generally
consistent with our discussion of the effect of reanalysis/
model datasets on Niño3.4 SST dispersion.
For Ensemble ONI, the largest influence of the
reanalysis datasets on increasing dispersion is between
about 18501900 (Figure 5) when fewer datasets
(Figure 4c) exist to constrain the uncertainty in Ensem-
ble ONI. Uncertainty during this period averages 0.45 K
when all 32 datasets are included, but 0.37 K when
excluding reanalysis/model datasets. The differences are
particularly large between 1850 and 1880 when uncer-
tainty averages 0.52 K (all datasets) and 0.41 K
(no reanalysis/model datasets). Said another way, includ-
ing reanalysis/model datasets increases uncertainty by
27% prior to 1880. From 1900 to 1950, the average
uncertainty is about the same, while for 19502000 the
increase averages 7% and from 2000present, reanalysis/
model datasets decrease the dispersion by 11%
(Figure 5bd). These results suggest that including more
datasets, regardless of their interdependencies, increases
dispersion in the Niño3.4 region and leads to a more
conservative uncertainty range for Ensemble ONI.
A way to assess the magnitude and time-evolution of
our reported uncertainty is to directly compare with the
comprehensive estimate of total uncertainty from
HadSST4 (Kennedy et al., 2019). In a month-by-month
comparison, our Ensemble ONI uncertainty is less than
HadSST4 Niño3.4 uncertainty in 49% of the 1842
months, and less than HadSST4 Extended Niño3.4 uncer-
tainty in 2.5% of the months (Figure 4b). The median
Ensemble ONI uncertainty prior to 1950 is 0.39, while
the corresponding Niño3.4 and Extended Niño3.4
FIGURE 5 (a) Uncertainty in Ensemble ONI using all 32 input datasets from Table 1 (pink-purple) and excluding 15 datasets in Table 1
with an asterisk that used modelling or reanalysis (green), both with their respective 12-month smoothed trend line overlaid. (b) The time
series from 1850 to 1880 for the difference in Ensemble ONI uncertainty calculated using 32 input datasets minus Ensemble ONI uncertainty
calculated using 17 input datasets, with a 10-year smoothed trend line overlaid in black. (c) As per (b) but for 18801930, noting the y-axis
scale difference. (d) As per (b) but for 19302021, noting the y-axis scale difference [Colour figure can be viewed at]
uncertainties for HadSST4 are 0.36 and 0.16, respectively.
After 1950, the median uncertainties are smaller and are
0.15, 0.13, and 0.05 for Ensemble ONI, HadSST4 Niño3.4,
and HadSST4 Extended Niño3.4, respectively. The differ-
ence in the HadSST4 calculations arises from far fewer
SST observations in the Niño3.4 region than our self-
defined Extended Niño3.4 region, consistent with the
documented observational minimum in the central and
eastern tropical Pacific evident in ICOADS (Freeman
et al., 2017) and HadSST4 (Kennedy et al., 2019) from the
lack of shipping channels that passed through Niño3.4
before the mid-1950s. In terms of magnitude, our 1-sigma
Ensemble ONI uncertainty more closely follows the
HadSST4 Niño3.4 region uncertainty both before and
after 1950, which is the more conservative estimate of
uncertainty in the comparison.
The correlation of Ensemble ONI uncertainty with
HadSST4 Niño3.4 uncertainty prior to 1950 is 0.42, while
after 1950 is 0.62, and similar (0.42, 0.71) for the
Extended Niño3.4 region. The magnitude of the correla-
tion is less important to this discussion than the relative
comparisons. Specifically, similar correlations would sug-
gest that using the Extended Niño3.4 region HadSST4
uncertainty as a lower bound for assessing our Ensemble
ONI uncertainty is reasonable, and we interpret HadSST4
uncertainty in Niño3.4 as being close to an upper bound
since multiple sources of uncertainty are included in the
calculation of total uncertainty reported by Kennedy
et al. (2019). While our uncertainty estimate for Ensem-
ble ONI is structural (deriving from interdataset variabil-
ity), we argue that because HadSST4 uncertainty
(Kennedy et al., 2019) in Niño3.4 is comparable, then this
is evidence that our uncertainty estimate is a reasonable
approximation of the true uncertainty in Ensemble ONI.
Furthermore, HadSST4 uncertainty is slightly (but consis-
tently) better correlated with our estimate of Ensemble
ONI uncertainty when reanalysis/model datasets are
included (Pearson correlation of 0.69 and 0.63, for
Niño3.4 and Extended Niño3.4 regions) rather than
excluded (0.63 and 0.56, for Niño3.4 and Extended
Niño3.4 regions) from our overall calculations (Table 1
and Figure 5), likely due to underdispersion when
excluding the 15 reanalysis/model datasets, as discussed
Another method to assess the accuracy of our Ensem-
ble ONI uncertainty (Figure 4) is by considering strong El
Niño events that were well-documented numerically
and/or through historical accounts (Kiladis and
Diaz, 1986; McPhaden, 1999; Huang et al., 2016a;
L'Heureux et al., 2017; Huang et al., 2020). Since the
Bjerknes feedback leading to an El Niño event is well-
understood (Bjerknes, 1966; McPhaden et al., 2020), we
argue that the state of the tropical Pacific in Niño3.4 prior
to a strong El Niño event must reflect SST anomalies that
themselves suggest the initiation of the Bjerknes feed-
back, and that this physical requirement helps constrain
the corresponding uncertainty as well. Specifically, in the
summer and early fall months prior to a strong El Niño
event, the Niño3.4 SST anomaly is very likely to be posi-
tive. By extension, the range of Ensemble ONI values
determined by any uncertainty estimate also is unlikely
to be realistic if the range extends to negative values prior
to the months of the peak of any strong El Niño event.
There are several strong El Niño events prior to 1950
that serve as physical constraint on the uncertainty
(Figure 6). The 18771878 El Niño is among the strongest
in the observational record (Huang et al., 2020; Sanchez
et al., 2020). It has been associated with prolific drought
and famine that affected India and China from 1876 to
1877 (Lin et al., 2020) when an estimated 3% of the
world's population died, comparable to the impact of the
19181919 influenza pandemic (Singh et al., 2018). Fur-
thermore, coral records from the central equatorial
Pacific found evidence of significant warming in the
northern Line Islands 1218 months before the peak of
the 18761878 El Niño event in late 1877 (Sanchez
et al., 2020), analogous to the well-documented 2015
2016 El Niño (Levine and McPhaden, 2016). Finally,
observed sea level pressure (SLP) at Santiago, Chile, were
already three standard deviations below normal in
September 1876 and remained below 1.5 standard devia-
tions for the rest of 1876, suggesting that a significant El
Niño event was already underway prior to 1877 (Kiladis
and Diaz, 1986). Therefore, the summerfall of 1877
should have positive Ensemble ONI values. The 1899
1900 El Niño was linked to drought in India and captured
the interest of Sir Gilbert Walker as he formulated the
framework of the Southern Oscillation (McPhaden
et al., 2020). The strong 191819 El Niño coincided with
the failure of the Indian Monsoon (Quinn et al., 1978;
Giese et al., 2010), and the 18881889 El Niño was associ-
ated with extreme famine in Ethiopia (Wolde-
Georgis, 1997) and corroborated by global written records
(e.g., Quinn, 1993; Garcia-Herrera et al., 2008).
In these aforementioned cases, we assert that the
summers preceding these El Niño events are physically
constrained to positive Ensemble ONI values, even when
the full range of uncertainty is considered (Figure 6).
Based on that assertion, our reported uncertainty range
(the 68.27% percentile range) is more appropriate than,
say, a larger percentile range corresponding to the 95%
interval (i.e., a 2-sigma range), because the larger percen-
tile range results in summers preceding strong El Niños
potentially having negative Niño3.4 SST anomalies.
Figure 6 highlights this discrepancy, with particular
attention on the 18771878 El Niño (Figure 6b). The
2-sigma uncertainty range in 1877 suggests that there
was potential for a developing La Niña as late as June or
July, which strongly contradicts evidence (Kiladis and
Diaz, 1986; Singh et al., 2018; Lin et al., 2020; Sanchez
et al., 2020) that one of the strongest El Niños on record
(Huang et al., 2020) had already begun developing.
Figure 6c shows the 18881889 El Niño and is a case
where the 1-sigma and 2-sigma uncertainty ranges both
suggest at least a positive Niño3.4 SST anomaly, but close
inspection shows that even in the summer 1888, the
lower bound of the 2-sigma range leans towards neutral
conditions, which is also unlikely given independently-
derived documentary evidence (Quinn, 1993; Wolde-
Georgis, 1997; Garcia-Herrera et al., 2008). Figure 6de
show even clearer cases that the 2-sigma range is too con-
servative, producing values of Niño3.4 SST anomalies
that are close to zero or even negative in the summers
preceding the well-documented 18991900 and 1918
1919 (Quinn et al., 1978; Giese et al., 2010) El Niños.
Hence, case studies of pre-1950s El Niños reveal substan-
tial evidence that the larger 2-sigma uncertainty range is
not consistent with the physics of the Bjerknes feedback
or with independently derived data and records.
4.2 |Evaluating ENSO events since 1850
We assess Ensemble ONI against the existing NOAA ONI
available from 1950 to 2020 (Barnston et al., 1997; Huang
et al., 2017) and against additional ENSO indices that
begin before 1950 (Figure S3). Differences compared to
NOAA ONI are within 0.23 K, and Ensemble ONI uncer-
tainty (Figure 4b) exceeds the difference in 75% of the
months. The correlation coefficients of pre-1950
FIGURE 6 (a) 18501950 time series of the Ensemble ONI (black line) bracketed by the 1-sigma percentile range (pink-purple shading)
and 2-sigma percentile range (grey shading), and (be) are similar to (a) but focused on four specific pre-1950 El Niño events: 18771878,
18881889, 18991900, and 19181919, respectively. The vertical lines on (be) are drawn at halfway through the year preceding the El Niño
and halfway through the year following the peak of the El Niño. In (b), the El Niño conditions were evident in some documentary records as
early as the summer of 1876, so the vertical lines extend over 2 years [Colour figure can be viewed at]
Ensemble ONI with the Relative Niño3.4 index (RelNiño;
van Oldenborgh et al., 2021), Cold Tongue Index (CTI;
Deser and Wallace, 1987), Southern Oscillation Index
(SOI; Ropelewski and Jones, 1987; Allan et al., 1991;
Troup, 1965), and the Bivariate EnSo Timeseries index
(BEST; Smith and Sardeshmukh, 2000) are 0.78, 0.78,
0.54, and 0.89, respectively (noting we multiplied SOI by
1 for comparison with SST-based indices). Post-1950,
SST-based indices have better correlations ranging from
0.71 to 0.99, almost certainly due in part to higher
coverage and quality of in situ observations. The highest
correlation is with NOAA ONI, which is the general
methodology that Ensemble ONI is based on but NOAA
ONI uses only a single input dataset. SOI is less
correlated with Ensemble ONI, likely since it is based on
sea-level pressure station data that contains a greater pro-
portion of high-frequency subseasonal atmospheric
TABLE 2 33 literature-based definitions of ENSO events
Institution/reference Criteria amplitude Criteria length
1 Ashok et al. (2007) >+/0.7σSST 3 tri-monthlies
2 Australian Bureau of Meteorology >+/0.8C SST Any month
3 Bove et al. (1998) >+/0.5C SST 6 consecutive pentads
4 Chen et al. (2004) and Yu et al. (2011) >+/1.0C SST Any month
5 Coelho et al. (2002) >+/2.0C SST Any month
6 Gergis and Fowler (2005) >+/0.5C SST 6 consecutive months
7 Hanley et al. (2003) El Niño: 25th %ile, La Niña: 75th %ile Any month
8 Hanley et al. (2003) >+/1.0σSST 4 consecutive months
9 Luan et al. (2012) >+/1.2σSST Any month
10 Lucena et al. (2011) >+/0.5C SST 2 consecutive tri-monthlies
11 McGregor et al. (2010) >+/0.5σSST Any month
12 Meehl et al. (2006) and Tian et al. (2017) >+/1.0σSST 1 tri-monthly
13 NOAA Climate Prediction Center >+/0.5C SST 5 consecutive tri-monthlies
14 Predybaylo et al. (2017) >+/1.4C SST 1 tri-monthly
15 Predybaylo et al. (2017) >+/1.0C SST 1 tri-monthly
16 Santoso et al. (2017) >+/0.5σSST 1 tri-monthly
17 Zanchettin et al. (2008) and Sullivan et al. (2016) >+/0.5C SST 1 pentad
18 Takahashi et al. (2014) >+/0.4C SST 3 consecutive months
19 Takahashi et al. (2014) >+/1.0C SST 3 consecutive months
20 Trenberth and Hoar (1997) >+/0.4C SST 6 consecutive tri-monthlies
21 Wang et al. (2000) >+/0.5C SST 8 months
22 Wang et al. (2019) >+/0.6C SST 1 pentad
23 Wolter and Timlin (2011) El Niño: 30th %ile, La Niña: 70th %ile 1 bi-monthly
24 Yeh et al. (2009) >+/0.5C SST 1 tri-monthly
25 Zanchettin et al. (2008) >+/0.4C SST 1 pentad
26 Zhang et al. (2015) >+/0.6σSST 1 tri-monthly
27 Phillips et al. (1999) >+/0.7σSST 4-month period
28 Song et al. (2016) >+/0.5σSST 4 consecutive months
29 Qian et al. (2011) and Voskresenskaya and
Marchukova (2017)
>+/0.5C SST 5 consecutive months
30 Hendon et al. (2009) >+/0.7σSST 1 tri-monthly
31 Kao and Yu (2009) >+/1.0σSST 3 consecutive months
32 JMA >+/0.5σSST 6 consecutive pentads
33 Turkington et al. (2019) >+/0.25C SST 5 successive tri-monthlies
The bias in Ensemble ONI relative to other indices
(Figure S3) is variable and tends to be smaller after about
1950, as expected given the confluence of all input data
sets as we move towards the present day (Figure S1).
There is a slight, but not constant, warm bias in Ensem-
ble ONI relative to SOI and BEST, and cold bias relative
to CTI. There is a slightly larger warm bias compared
with RelNiño, but the bias time series varies with notable
warm biases (Ensemble ONI is warmer than RelNiño)
prior to 1870 and during WWII, and cold biases in the
early 1900s. The nonsystematic biases suggest differences
that arise from the input data itself and not simply arte-
facts arising from methodological differences, again
speaking to the explanatory power of interdataset vari-
ability being the leading source of uncertainty in
Ensemble ONI.
To better characterize ENSO events, we apply a large
ensemble-based approach again, but this time to 33 litera-
ture-based definitions of ENSO, minimizing subjectivity in
our interpretation of the data. These definitions of ENSO
state (Table 2) are applied to Ensemble ONI to determine
time periods that correspond to ENSO events. We apply
the definitions to Ensemble ONI and count the years with
ENSO events by assuming that any year, defined as the
boreal winter months of DecemberFebruary, is an El
Niño yearor La Niña yearwhen there is at least
1 month that meets greater than 60% of definitions. With
this simple counting method, 42 El Niños and 35 La Niñas
have occurred between 1850 and 2021, or about one El
Niño or La Niña event every 45 years, which near the
middle of the 27 year return frequency in past studies
(Quinn et al., 1978; Ramesh and Murtugudde, 2013; San-
toso et al., 2017). Applying the definitions to the Ensemble
ONI calculated using the full uncertainty range (Figure 4)
results in El Niño frequency ranging from 3 to 6 years,
and La Niña frequency ranging from 3 to 9 years, which is
consistent with the literature-based return frequency
(e.g., McPhaden et al., 2020). Using the aforementioned
ENSO year and literature-based definitions, we find that
the transition from El Niño to La Niña occurs about twice
as frequently as La Niña to El Niño, which is also consis-
tent with previous work (Kessler, 2002; Ohba and
Ueda, 2009; Okumura and Deser, 2010; Ohba and
Watanabe, 2012; Choi et al., 2013; An and Kim, 2017; An
and Kim, 2018). Figure 7a shows the time series of the
fraction of definitions met (values in Table S4).
FIGURE 7 18502021 time series of (a) the percent agreement with 33 literature-based definitions (see Table 2) of El Niño (positive
values in light red) and La Niña (negative values in light blue), and the percent agreement with multiple proxy records of ENSO events from
1850 to 1977 (black lines), and (b) the Ensemble ONI values shaded to only show El Niños (red) and La Niñas (blue) when greater than 60%
of the literature-based definitions are met. Values in grey are when neither El Niño or La Niña conditions are met by greater than 60% of the
definitions [Colour figure can be viewed at]
Moving beyond literature-based definitions, we aug-
ment our evaluation of ENSO events by comparing
Ensemble ONI to proxy and documentary records of both
El Niño and La Niña events. Comparison of the Ensem-
ble ONI with proxy records can also help us understand
uncertainties in the instrumentally based measures of
ENSO (Anderson et al., 2013; Kennedy, 2014). The power
in this comparison is that proxy records are completely
independent from the input data we used (Table 1) for
Ensemble ONI, and less susceptible to potential under-
dispersiveness that may arise from shared biases between
SST datasets and reanalysis due to their similar methodo-
logical constructions (Kennedy, 2014; Pillar et al., 2018).
Challenges in comparing Ensemble ONI with proxy
records are that the proxy results are reported at annual
time resolution (McGregor et al., 2013), they may be sen-
sitive to manifestations or flavours of ENSO that depart
from Niño3.4 (Ashok et al., 2007), and they are indirectly
connected to SST anomalies. As we discuss below, how-
ever, the alignment of proxy-based ENSO events with
both ENSO events and their uncertainty from Ensemble
ONI is excellent.
We use proxy and written records from 13 literature
sources, all of which document El Niños and 11 document
La Niñas, during an overlapping period between 1850
and 1977 (Quinn et al., 1987; Stahle et al., 1998; Cook
et al., 2008; Braganza et al., 2009; Gergis and
Fowler, 2009; McGregor et al., 2010; Wilson et al., 2010;
Li et al., 2011; Hakim et al., 2016; Anderson et al., 2019;
Freund et al., 2019; Tardif et al., 2019; Dätwyler
et al., 2020; Sanchez et al., 2020). Most of these proxy
records have different units and variables, and the writ-
ten records are qualitative in their classification of ENSO.
To facilitate a comparison amongst them and Ensemble
ONI, we use an approach modified from Wolter and
Timlin (2011) and analyse the annual percentile ranks
from Ensemble ONI and from each proxy records during
the 18501977 (Table S5), when they all overlap. The
upper, middle, and lower terciles represent El Niño, Neu-
tral ENSO, and La Niña, respectively, except for Stahle
et al. (1998) which is an SOI-based proxy index with the
signs inverted. We then analyse the percentage of proxies
that meet this percentile-based definition of ENSO as a
function of time.
In Figure 7a, the black line overlaid on the fraction of
literature-based ENSO definitions shows the fraction of
the proxies (values in Table S5) that reported an El Niño
(out of 13 proxies) or La Niña (out of 11) between 1850
FIGURE 8 The 20 (a) strongest
El Niño events, and (b) strongest La
Niña events (1850 to May 2021)
determined by the peak value of
Ensemble ONI (squares) between
October and March of the given year.
Error bars are the asymmetric
uncertainty determined by the spread
across input data sets [Colour figure
can be viewed at]
and 1977, noting that the proxy results are at annual time
resolution. There are 42 El Niños and 41 La Niñas of
varying strength and duration. Remarkably, there are
only two ENSO events, both of which are La Niñas, that
were not identified by any proxy but that met at least 50%
of the literature-based definitions of being La Niñas:
18561857 and 19031904. The 18561857 event has
105% uncertainty while the 19031904 has 58% uncer-
tainty (Figure 4) around the central Ensemble ONI value,
where the percentages are calculated as the average
uncertainty from July to March divided by the average
Ensemble ONI from July to March. These are high rela-
tive to uncertainty in ENSO events in their respective
time periods (93% on average from 1850 to 1869, and 37%
from 1890 to 1909) so there is the possibility that the
Ensemble ONI reported an overly strong La Niña state,
or that a La Niña did occur and did not manifest in the
proxy records. The main conclusion from Figure 7, how-
ever, is that the vast majority of ENSO events are sub-
stantiated by completely independent records from
multiple proxies.
Acknowledging the results of our uncertainty assess-
ment, and of the comparisons with literature-based defini-
tions and proxy-based findings, we take the Ensemble ONI
values and categorize El Niño and La Niña events since 1850
based on the maximum Ensemble ONI during the months
of OctoberMarch. We specify StrongEl Niños when peak
values range from +1:5Cto+2:0C, and StrongLa
Niñas are values are less than 1:5C, while SuperEl
Niños are events with peaks exceeding +2:0C. Due to
their climatic and large-scale impacts, Super El Niños are
a category that is discussed in the literature beyond our
operational classification (Latif et al., 2015; Chen
et al., 2016; Bing and Xie, 2017; Hameed et al., 2018).
We identify six Super El Niños (Figure 8a) in 1877
1878, 19971998, 20152016, 19821983, 19721973, and
18881889. The top four all sustained average Ensemble
ONI values greater than +2:0C from OctoberMarch,
separating them from the 19721973 and 18881889
events. The top three Super El Niños have uncertainty
ranges that statistically distinguish them from other
ENSO events since 1850. Furthermore, the Super El
Niños have well-documented global temperature anoma-
lies (Morice et al., 2021) and impacts (Slingo and
Annamalai, 2000; van der Werf et al., 2004; Zhu
et al., 2018). Figure S4 shows the results in Figure 8 but
compares the magnitude of the peak ENSO event ampli-
tude from NOAA ONI (Barnston et al., 1997) and Rela-
tive Niño3.4 (van Oldenborgh et al., 2021), both of which
rely on a single input dataset as opposed to the 32 input
datasets for Ensemble ONI. NOAA ONI top 20 results are
very similar, but Relative Niño produces more super El
Even though the 19881989 and 19161917 La Niñas
are the strongest since 1850 (Figure 8b), we do not sepa-
rate Super from Strong La Niñas because uncertainty in
Ensemble ONI prevents a clear distinction between these
and slightly weaker, but still intense, La Niñas. Figure 8
also shows how the asymmetric uncertainty derived from
interdataset variability could result in some of the top
20 ENSO events prior to 1950 shifting towards Super El
Niños (18961897, 19021903) or towards Moderate El
Niños (19301931, 18991900) as more data is
All the top 20 El Niño events were recorded in the
proxy records summarized in Figure 7, with an average
of 81% of the proxies reporting an El Niño event in the
top 20 years compared to an average of 52% in non-top
20 years. Similarly, all but the 18561857 La Niña event
(0 proxies noted this) were recorded in the proxy records
(Figure 7), with an average of 77% reporting La Niñas in
the matching years of top 20 La Niñas compared to an
average of 46% in non-top 20 years. The interpretation is
that top 20 events are more likely to be evident in docu-
mentary sources, proxy records, and physical state vari-
ables such as SST anomalies in the Niño3.4 region, and
that Ensemble ONI provides realistic estimates of ENSO
even when in situ SST observations are scarce in the 19th
and early-mid 20th centuries.
The Ensemble Oceanic Niño Index (Ensemble ONI) uses
an ensemble of input datasets to develop a new ENSO
index based on Niño3.4 region SST anomalies. We use
methods that are similar to the existing NOAA ONI
(Barnston et al., 1997), but our work incorporates an
ensemble of 32 input SST datasets (Table 1), extends back
to the year 1850, is available in near real-time, and uses
the ensemble to quantify uncertainty (Figure 4).
Our methodology uses both a median of shifting base-
period climatologies (to account for warming evident in
the Niño3.4 region, Figure S1) and a median SST anom-
aly across the ensemble of 32 datasets. The choice of a
median helps resist the influence of outliers that may
arise in any month since 1850 within individual datasets.
In other words, our methods do not attempt to diagnose
issues, artefacts, or methodologies of each dataset, but
instead consider what the ensemble as a whole suggests
about Niño3.4 SSTs. Our methods can thus easily ingest
additional data both backwards in time and as the future
unfolds. A larger ensemble of values in any given month
strengthens the central estimate of the Ensemble ONI,
and we find that the variability across all datasets, regard-
less of similarities in the underlying in situ SST, produces
the most conservative uncertainty range (Figures 4
and 5).
We discuss the time evolution of our estimates of
uncertainty, and compare the magnitude to HadSST4
total uncertainty (Kennedy et al., 2019) for the Niño3.4
region. We also advance the discussion of uncertainty
estimate by arguing that our best estimates are semi-
quantitatively constrained by well-documented ENSO
events, a suite of 33 different literature-based definitions,
and comparisons with 13 proxy records of ENSO events
since 1850 (Figure 7). We argue that our uncertainty,
which is determined from the variability across our
32-dataset ensemble, offers a way to refine future esti-
mates of uncertainty in ENSO indices against diverse
Our results show that there is a statistical distinction
between Super El Niños and Strong ones, but that the
same does not apply to La Niñas. In terms of ranking, the
18771878 El Niño is the strongest, but uncertainty in the
peak magnitude of the event does not rule out the possi-
bility that the 19821983, 19971998, or 20152016 El
Niños were at least as strong, consistent with Huang
et al. (2020). However, our findings also do not rule out
the possibility that 18771878 was even stronger than our
central estimate (e.g., Sanchez et al., 2020), as severe
undersampling in the core of the central Pacific and simi-
larity in methods amongst SST dataset curators may be
underestimating the trueamplitude of the event
(Kaplan et al., 1998). Coral records, for example, from the
Fanning and Palmyra Islands (Sanchez et al., 2020) sug-
gest that the magnitude of the 18771878 El Niño was
more than one standard deviation greater than the next
strongest El Niño (19971998). In any case, those four El
Niños are very likely Super El Niños in terms of peak
magnitude, conditions averaged over boreal winter
months, and documented global impacts.
Consistent with ENSO asymmetry (An and
Kim, 2018), we do not make a distinction for SuperLa
Niñas, but the two strongest are 19161917 and 1988
1989, with the latter being so intense that it led to the
establishment of the term La Niña in literature over
30 years ago (Philander, 1990; McPhaden et al., 2015).
Finally, the time span of our work shows that El Niño
events were more intense at the turn of both the 20th and
21st centuries, with a lull in the mid-1900s (Figure 3), sim-
ilar to other studies (Giese and Ray, 2011; Wolter and
Timlin, 2011; McGregor et al., 2013), some of which have
suggested this centennial-scale variability in ENSO ampli-
tude has existed for at least the last few-several hundred
years or more (Enfield and Cid, 1991; Garcia-Herrera
et al., 2008; Langton et al., 2008; Li et al., 2011; Tindall
et al., 2016; Liu et al., 2017), although our work does
not attempt to diagnose the causal mechanism
(e.g., Karnauskas et al., 2012; Lewis and LeGrande, 2015)
and may be underestimating centennial-scale variability
(e.g., Wittenberg, 2009; Ault et al., 2013).
The Ensemble ONI and its associated uncertainty
(Figure 4) establish a reliable baseline with which to com-
pare and possibly constrain other SST datasets, reanalysis
models, proxy and documentary reconstructions, as well
as climate models. Future developments of the Ensemble
ONI include incorporating pre-1850s data as new records
are compiled (Giese et al., 2016; Freeman et al., 2017;
Slivinski et al., 2019), exploring flavours of El Niño (Ashok
et al., 2007), re-calculation of ONI relative to tropical
ocean warming trends (van Oldenborgh et al., 2021), and
further refining the uncertainty estimation for pre-1950s
data. Additional or updated reanalysis or observational
datasets can also be incorporated readily, and our methods
be adaptable to explore other Niño regions to address
dynamical interconnections (Xie et al., 2009; Ummenhofer
et al., 2011). Finally, our methods could be adapted to
combine SST data with additional dimensions (Wolter and
Timlin, 2011), such as long-term records of sea level pres-
sure (Quinn et al., 1978; Ropelewski and Jones, 1987;
Allan et al., 2002; Cram et al., 2015), for a multivariate-
ensemble ENSO index.
We thank KNMI Climate Explorer for maintaining their
data repository for exploring large datasets. The data used
in this study are available in Table S3.
The authors declare no potential conflict of interest.
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Additional supporting information may be found in the
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How to cite this article: Webb, E. J., & Magi, B.
I. (2022). The Ensemble Oceanic Niño Index.
International Journal of Climatology,121. https://
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We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature data set, HadCRUT5. HadCRUT5 presents monthly average near-surface temperature anomalies, relative to the 1961–1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea-surface temperature (SST) measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in SST measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200-member ensemble accompanied by additional uncertainty information. The combination of revised input data sets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases in SST measurements from ships. These updates result in greater consistency with other independent global surface temperature data sets, despite their different approaches to data set construction, and further increase confidence in our understanding of changes seen. © 2020 Crown Copyright. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.