PUBLICATIONS
Geophysical Research Letters
RESEARCH LETTER
10.1002/2016GL067935
Key Points:• There is disagreement between
observed and simulated global meancooling response to recent volcanic
eruptions• Accounting for the observed
coincidence of El Niño events withrecent eruptions resolves this bias
• Detection and attribution studies
must consider sampling biases arisingfrom internal variability

Supporting Information:• Data Set S1• Data Set S1• Supporting Information S1
Correspondence to:F. Lehner,
ﬂehner@ucar.edu

Citation:
Lehner, F., A. P. Schurer, G. C. Hegerl,
C. Deser, and T. L. Frölicher (2016),
The importance of ENSO phase during
volcanic eruptions for detection andattribution, Geophys. Res. Lett., 43,
doi:10.1002/2016GL067935.
Received 22 JAN 2016
Accepted 6 MAR 2016
Accepted article online 10 MAR 2016

The importance of ENSO phase during volcanic
eruptions for detection and attribution
Flavio Lehner1, Andrew P. Schurer2, Gabriele C. Hegerl2, Clara Deser1, and Thomas L. Frölicher3
1

Climate Analysis Section, National Center for Atmospheric Research, Boulder, Colorado, USA, 2School of Geosciences,
University of Edinburgh, Edinburgh, UK, 3Environmental Physics, Institute of Biogeochemistry and Pollutant Dynamics,
ETH Zürich, Zurich, Switzerland

Abstract Comparisons of the observed global-scale cooling following recent volcanic eruptions to thatsimulated by climate models from the Coupled Model Intercomparison Project 5 (CMIP5) indicate that themodels overestimate the magnitude of the global temperature response to volcanic eruptions. Here we showthat this overestimation can be explained as a sampling issue, arising because all large eruptions since1951 coincided with El Niño events, which cause global-scale warming that partially counteracts thevolcanically induced cooling. By subsampling the CMIP5 models according to the observed El Niño–Southern
Oscillation (ENSO) phase during each eruption, we ﬁnd that the simulated global temperature response tovolcanic forcing is consistent with observations. Volcanic eruptions pose a particular challenge for thedetection and attribution methodology, as their surface impacts are short-lived and hence can be confounded
by ENSO. Our results imply that detection and attribution studies must carefully consider sampling biases
due to internal climate variability.

1. Introduction
Detection and attribution are a key tool used for identifying the factors that contribute to observed changes
in climate over the past century and for quantifying the response to individual forcings, such as greenhousegases, aerosols, or volcanic eruptions [Bindoff et al., 2013; Hasselmann, 1993]. Estimates of the relative contributions by anthropogenic and natural forcings inform our attempts to constrain transient and equilibrium
climate sensitivity [Collins et al., 2013]. Yet such attempts are inherently complicated by the fact that we
observe only one of many possible climate trajectories. With internal variability contributing signiﬁcantly
to decadal-scale trends in certain quantities and regions, robust attribution of climate change can becomechallenging and sensitive to the particular realization of variability sampled in the observational record[Deser et al., 2012a].
Using the Coupled Model Intercomparison Project 5 (CMIP5) [Taylor et al., 2012] multimodel ensemble,
studies have estimated the contribution of natural forcing, such as explosive volcanic eruptions andvariations in solar irradiance, to the evolution of global mean surface temperature (GMST) since 1951
[Jones et al., 2013; Gillett et al., 2013; Johansson et al., 2015a]. Scaling the model simulations to matchthe observed global mean surface temperature record, these studies concluded that the CMIP5 models
as a group overestimate the global temperature response to natural forcing by a factor of 2, with uncertainties that render it highly unlikely that the multimodel mean is consistent with the observed record.
Since variations in solar irradiance during this time period are small, the models are suspected to produce
excessive cooling mainly in response to volcanic eruptions [Bindoff et al., 2013]. Paleoclimate model-datacomparisons also ﬁnd that models have a larger volcanic response than suggested by many [Schurer et al.,2013], although not all reconstructions [Hegerl, 2003]. Large uncertainties remain with these comparisons,
including how tree ring data reﬂect short-term volcanic cooling [Stoffel et al., 2015]. Hence, the question
arises whether climate models systematically overestimate the response to volcanism and what thecauses might be.

©2016. American Geophysical Union.
All Rights Reserved.

LEHNER ET AL.

The sample size used in the evaluation of the models’ response to volcanic eruptions is very small, with only
three large tropical volcanic eruptions having occurred since 1951: Agung in March 1963, El Chichon in April
1982, and Pinatubo in June 1991. In addition, these three eruptions all coincided with El Niño events, heavily
biasing the observed sample toward El Niño as the climate background state (Figure 1). The backgroundclimate state can be important for the response of the climate system to an eruption [Ding et al., 2014;

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Figure 1. Coincidence of volcanic eruptions with El Niño events. Observed Nino3.4 sea surface temperature anomaly index(http://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/nino34.long.anom.data; units of standard deviations) andaerosol optical depth [Sato et al., 1993] during the eruptions of (a) Agung, (b) El Chichon, and (c) Pinatubo. Light grayshading indicates the interval between the eruption start date and February of the following year. Horizontal dashed line
indicates the El Niño selection criterion (1 standard deviation of the Nino3.4 index).

Zanchettin et al., 2013; Frölicher et al., 2013]. Recent research suggests an increased likelihood for El Niño inthe year of or following a volcanic eruption, with a subsequent increased likelihood for La Niña in the year
following the El Niño [Adams et al., 2003; McGregor and Timmermann, 2011; Li et al., 2013; Lehner et al.,2015; Maher et al., 2015; Chikamoto et al., 2016]. Many studies rely at least in part on the dynamical thermostat
mechanism to explain this [Clement et al., 1996]. However, some modeling studies suggest that the initial
tropical Paciﬁc sea surface temperature response to a volcanic eruption resembles La Niña, but the sea
surface height response is more indicative of El Niño [McGregor and Timmermann, 2011; Maher et al., 2015].
It is worth noting that considerable uncertainties exist regarding the chronology of events in the tropicalPaciﬁc after an eruption, the dominating processes, and the scalability of these with eruption strength.
In any case, the El Niño events following El Chichon and Pinatubo were already underway before the time ofthe eruption [Self et al., 1997]. Fully coupled simulations and paleoclimate records suggest that the coincidence of El Niño events with three consecutive volcanic eruptions of the same or greater magnitude asAgung, as was the case during 1951–2014, is a very rare occurrence (see section S1 in the supporting information). Also, sampling El Niño–Southern Oscillation (ENSO) variability itself in coupled climate models
requires large ensembles or long control simulations [Wittenberg, 2009; Deser et al., 2012b; Stevenson et al.,2012]. This issue is exacerbated by volcanic forcing, which causes a GMST response on a similar timescale
as ENSO, with tropical Paciﬁc surface temperatures dominating the initial response in GMST. Hence, the effectof volcanic forcing can be confounded by the chance coincidence of an ENSO event [Santer et al., 2001, 2015;
Johansson et al., 2015b], something that is not explicitly considered in traditional detection and attribution
studies. Linearly removing the ENSO signal from the GMST record [Thompson et al., 2009] would not capture
any state dependence or nonlinearity of the response.
El Niño events typically cause positive anomalies in GMST, and one might expect that a cooccurrence of
El Niño with a volcanic eruption would act to reduce the cooling that arises from the negative radiative
forcing of the volcanic aerosols. Here we do not address the question of whether volcanic eruptions cause
El Niño events. Rather we investigate the inﬂuence of ongoing El Niño events on GMST during the threestrong eruptions since 1951. Speciﬁcally, we aim to answer the question whether detection and attributionof the inﬂuence of natural (volcanic) forcing using CMIP5 models is biased by the unusual realization of
ENSO phase during these eruptions. While detection and attribution studies are cautious to interpret
results, particularly if based on periods as short as 1951–2010 [Ribes and Terray, 2013; Bindoff et al.,2013], the importance of ENSO phase during volcanic eruptions for detection and attribution has not been
investigated systematically. To do this, we subsample the model simulations according to ENSO phase andapply simple superposed epoch analysis as well as standard detection and attribution algorithms. The
remainder of this paper is structured as follows. Section 2 introduces the data and methods used,section 3 presents the consequences of the subsampling, and section 4 summarizes the results anddiscusses their implications.

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2. Data and Methods
2.1. Observations and Model Simulations
We use HadCRUT4 [Morice et al., 2012] as surface air temperature observations. Besides HadCRUT4 we also
tested other observational data sets (HadCRUT4 inﬁlled [Cowtan and Way, 2014], GISTEMP [Hansen et al.,2010], BEST [Rohde et al., 2012], and MLOST [Vose et al., 2012]); however, since the results are not dependent
on the data set used (see Figure S1), we do not discuss them further.
We use monthly mean 2 m air temperature from all model simulations with the tag “historical” and “historicalNat”
from the CMIP5 archive (see Tables S1–S3 for details). All model output is bilinearly regridded to 5° × 5° andthen masked according to where observations exist in the HadCRUT4 data set on a monthly basis.
In addition to CMIP5 we use the 40-member Community Earth System Model (CESM1) Large Ensemble[Kay et al., 2014]. The CESM1 Large Ensemble is initiated in 1920 and follows the conventional historicalforcing protocol of CMIP5. In this ensemble, each member starts from slightly different initial conditions, created by small changes in atmospheric temperature (on the order of 1014°C).
Finally, we make use of a 10-member “Pacemaker” ensemble with CESM1. In these simulations the easternequatorial Paciﬁc sea surface temperature (SST) anomalies are nudged toward observations (20°S–20°N
and 180°W to the American coast), leading to a simulated ENSO evolution in line with observations (asopposed to the free-running simulations of CMIP5 or the CESM1 Large Ensemble, which simulate theirown random chronologies of ENSO events). The CESM1 Pacemaker ensemble was conﬁgured following
Kosaka and Xie [2013], using ERSSTv3b for the SST nudging, and starting in 1920.
2.2. Subsampling According to ENSO Phase
As an indicator of ENSO phase, we use the monthly Nino3.4 index. We calculate the Nino3.4 index by averaging the simulated SSTs over the region 170°W–120°W and 5°S–5°N, detrending the resulting time series
using a quadratic ﬁt, and then normalizing the detrended Nino3.4 index. The use of Nino3 (150°W–90°W,
5°S–5°N) and Nino4 (160°E–150°W, 5°S–5°N) indices instead of Nino3.4 does not alter our results (not shown).
We consider the three largest volcanic eruptions since 1951: Agung, El Chichon, and Pinatubo. In all cases, an
El Niño event (deﬁned when Nino 3.4 index > 1σ) occurred during the boreal winter (December to February)
immediately following the eruption and decayed in the following spring. We subsample the model simulations to mimic this coincidence by considering only simulations that have a Nino3.4 index > 1σ in any ofthe months December, January, or February after the eruption start month.

3. Results
3.1. Average Volcanic Response
In order to identify the average response of GMST to a major volcanic eruption, we conduct a superposed
epoch analysis. The GMST response to each volcanic eruption is calculated as anomalies relative to the 5 year
average preceding the eruption start date. For CMIP5 we construct the multimodel mean response by
averaging across all available simulations. Finally, we average over the three eruptions to get the mean
volcanic response.
Figure 2a shows the mean GMST response to the three eruptions (Agung, El Chichon, and Pinatubo) in observations and CMIP5. The full CMIP5 multimodel mean suggests an average cooling of 0.24°C [0.39, 0.08;
5–95% range] after an eruption (measured as the ﬁrst posteruption minimum in GMST; blue line in Figure 2a).
When only those CMIP5 simulations that show an El Niño event during the eruption are selected, the coolingis reduced signiﬁcantly to 0.16°C [0.42, 0.11] (red line in Figure 2a; 95% signiﬁcance level), in closer agreement with observations (0.11°C; black line in Figure 2a). The timing of the cooling in the subsampled CMIP5
simulations is also in better agreement with observations, with the ongoing El Niño reducing the cooling initially and moderating it over the course of approximately the next two years. Beyond two years, the full andsubsampled CMIP5 multimodel means become statistically indistinguishable. The uncertainty range of thesubsampled CMIP5 ensemble is larger than that of the full CMIP5 ensemble, since after subsampling fewer
simulations go into the multimodel mean and fewer simulations go into the composite of three volcanoes(see Table S1). Note that the observations are noisier than the multimodel mean as they consist of only threesamples and can be inﬂuenced by a single El Niño event, such as the strong one that occurred in 1982/1983

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Figure 2. Observed and simulated global mean surface temperature (GMST) anomaly relative to the mean of the 5 years
preceding the eruption start date (dashed vertical line) averaged over the three recent volcanic eruptions (Agung,
El Chichon, and Pinatubo). (a) CMIP5 historical simulations (all ENSO phases: blue curve and shading; El Niño events
only: red curve and shading), and (b) CESM1 Large Ensemble (color coding as in Figure 2a) and Pacemaker simulations(green curve and shading). The solid and dashed black curves show the observations before and after adjusting for theinﬂuence of ENSO, respectively [see Thompson et al., 2009]. The colored shading gives the 5–95% conﬁdence interval forthe model simulations. The months during which the blue and red curves are signiﬁcantly different (t test, 95% conﬁdence)are indicated in bright green along the x axis. The number of ensemble members considered is given in brackets. Time
series have been smoothed with a triangular 1-2-1 ﬁlter for visual purposes only.

(Figure 1b). Importantly, the 5–95% range of the subsampled CMIP5 simulations encompasses the warming peak from the ongoing El Niño event in observations, while the 5–95% range from the full CMIP5 doesnot (Figure 2a).
A number of studies have used statistical methods to remove the effects of ENSO and other internal modes ofvariability from GMST time series [Thompson et al., 2009, and references therein]. In Figure 2a the ENSOadjusted GMST from Thompson et al. [2009] (using HadCRUT3) is shown alongside the raw observationaltime series. The timing and amplitude of the posteruption cooling in the adjusted time series is similar to
the unconstrained CMIP5 ensemble, indicating that statistical and model-subsampling approaches yieldconsistent results.
CMIP5 results may be confounded by the fact that only a subset of models/simulations are sampled when
the criterion of the “correct” (same as observed) ENSO phase is applied. Hence, model structural differences
are not equally sampled in the two multimodel means. To address this issue, we repeat the analysis with
the CESM1 Large Ensemble, thereby ruling out model structural differences (Figure 2b). The full CESM1
ensemble has an average temperature response to volcanic eruptions comparable to the CMIP5 multimodel
mean (0.23°C [0.32, 0.11]). Again, when considering only the simulations with the correct ENSO phase,
this response is signiﬁcantly reduced by about 50% (0.12°C [0.36, 0.08]), in better agreement withobservations.
To further investigate whether ENSO phase during an eruption is indeed the dominant factor underlying theimproved agreement with observations, we turn to the so-called Pacemaker simulations with CESM1. Thesesimulations have the observed ENSO phase at all times by design, as the sea surface temperature (SST)
anomalies in the eastern equatorial Paciﬁc are nudged toward observations, while elsewhere the coupledmodel remains unconstrained. Indeed, the 10-member Pacemaker ensemble shows a similar evolution as
the subsampled CESM1 ensemble as well as good agreement with observations (Figure 2b). However, noneof the Pacemaker simulations reproduce the magnitude of the observed warming peak 6–9 months after thecomposite eruption, a feature that is driven primarily by the months following Agung, which saw strong
extratropical warm anomalies while the El Niño event was only of moderate magnitude (see Figure S7). It
is unclear whether these anomalies are at all related to the eruption or the El Niño event. It is possible that
10 Pacemaker simulations are not enough to sample the full range of GMST variability. However, we note that
adding the eruption of Santa Maria in 1902, another coincidence of a volcanic eruption and an El Niño eventto the observed composite mutes this feature considerably (see Figure S8). Unfortunately, the Pacemakersimulations do not include this eruption as they start in 1920.

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The uncertainty range for the Pacemaker simulations is substantially smaller than for the subsampled CESM1
ensemble, indicating that the Pacemaker simulations are more constrained than the subsampled CESM1. This
is not surprising given that every Pacemaker simulation undergoes the same evolution of tropical Paciﬁc SST
anomalies by design, and because tropical Paciﬁc SST anomalies are the leading cause of interannual GMST
variability [Stouffer et al., 2000]. In the subsampled CESM1 ensemble, on the other hand, different magnitudes
and ﬂavors of El Niño can occur that lead to a larger range of temperature responses. However, constraining
the ENSO phase in a given model (either via subsampling or nudging) does not guarantee improved agreement with observations, as shown by the results based on the equivalent set of simulations with the
Geophysical Fluid Dynamics Laboratory (GFDL) models (see supporting information section S6).
3.2. Detection and Attribution of Volcanic Response
Having established the inﬂuence of ENSO phase on the GMST response to volcanic eruptions, we turn to thedetection and attribution framework in order to estimate the magnitude of the volcanic and anthropogenic
ﬁngerprint from observations and investigate if ENSO phase indeed leads to a reduced posteruption coolingin observations, or whether the discrepancy suggested by the superposed epoch analysis could be due toaliasing an aspect of the forced response.
Detection and attribution perform a multiple regression of the observations onto different forced responses,
so-called “ﬁngerprints,” which are here derived from the multimodel mean across simulations subject tonatural-only (historicalNat) and natural-and-anthropogenic forcings (historical). The regression is based ona total least squares approach to account for natural variability [Allen and Stott, 2003; Schurer et al., 2013]
and estimates a scaling factor βi to best match the time-dependent ﬁngerprint Xi(t) to the observations
Y(t), taking into account uncertainty in the form of internal variability, in observations v(t) and modelﬁngerprints vi(t)Y ðt Þ ¼ ∑ m
ðX ðtÞ  v i ðt ÞÞβi þ v ðtÞ
i¼1 i

(1)

Uncertainty ranges for the scaling factors are determined by a Monte Carlo technique, where random
samples of internal variability from the CMIP5 “piControl” simulations are added to an estimate of the(noise-reduced) model ﬁngerprints and observations [Schurer et al., 2013]. Here we use globally averaged
data as ﬁngerprints; however, our results are very similar to studies that additionally used spatial information[Gillett et al., 2013]. The resulting scaling factors determine the magnitude of the forced response to naturalforcings only (historicalNat) and to natural and anthropogenic forcing combined (historical); following fromthe general form in (1):Y ðt Þ ¼ ðX historical ðtÞ  ν1 ðt ÞÞ βhistorical þ ðX historicalNat ðtÞ  ν2 ðtÞÞ βhistoricalNat þ v ðtÞ(2)

From these values, assuming linearity, we can calculate a scaling factor for the response to anthropogenic
forcing (“ANT”) and natural forcing (“NAT”), βANT and βNAT, by transforming the scaling factors for historicaland historicalNat simulations, βhistorical and βhistoricalNat, to those for ANT and NAT separately (accounting
for the natural forcing within the historical simulation), using the relationship Xhistorical(t) = XANT(t) + XNAT(t)[Tett et al., 1999]:
Y ðtÞ ¼ X ANT ðtÞ βhistorical þ X NAT ðtÞð βhistorical þ βhistoricalNat Þ  ν1 ðtÞβhistorical  ν2 ðt ÞβhistoricalNat þ v ðt Þ (3)This implies that βhistorical provides the scaling factor for XANT, thus
βANT ¼ βhistorical(4)

Finally, the scaling factor for XNAT, βNAT, is calculated from βhistorical and βhistoricalNat (section S9 in the supporting
information of Tett et al. [1999]):
βNAT ¼ βhistorical þ βhistoricalNat(5)

Detection and attribution results for continuous time series spanning 1956–2005 (see Figure S2) are very
similar to previous studies [e.g., Bindoff et al., 2013; Gillett et al., 2013]: the magnitude of the anthropogenic
multimodel mean response is detectable in observations (scaling factors which determine the magnitudeof the ﬁngerprint are greater than zero, p > 0.05; Figure 3a). The range of possible scaling factors
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encompasses one, suggesting that the amplitudeof anthropogenic response in models and observations is consistent. The results for natural forcing, on the other hand, show that the response
is detectable, but its magnitude is signiﬁcantly
stronger in the models than in the observations
by a factor of 2 (scaling factor of about 0.5;
Figure 3a).

Acknowledgments
We thank John Fasullo, Angeline
Pendergrass, Axel Timmermann, andDavid Paynter for discussion andTingting Fan and Yu Kosaka for providing the Pacemaker simulations (CESM1and GFDL, respectively). We thank two
anonymous reviewers for helpful comments. The National Center for
Atmospheric Research is sponsored by
the National Science Foundation. F.L. is
supported by an Early Postdoc Mobility
fellowship from the Swiss National
Science Foundation (SNSF). G.H. and
A.S. are supported by the ERC funded
project TITAN (EC-320691). G.H. wasfurther supported by NCAS and theWolfson Foundation and the Royal
Society as a Royal Society Wolfson
Research Merit Award (WM130060)
holder. T.L.F. acknowledges support by
the SNSF (Ambizione grant PZ00PZ14573). We acknowledge the World
Climate Research Programme’s Working
Group on Coupled Modelling, which is
responsible for CMIP, and we thank theclimate modeling groups for producing
and making available their model output. For CMIP, the U.S. Department ofEnergy’s Program for Climate Model
Diagnosis and Intercomparison provides coordinating support and led
development of software infrastructure
in partnership with the Global
Organization for Earth System Science
Portals. The scripts to generate key ﬁgures are provided as supplementaryﬁles, and the CMIP5 data are available
under https://pcmdi.llnl.gov/projects/
esgf-llnl.

LEHNER ET AL.

In order to test the effect of ENSO phase on our
results, we repeat the detection and attribution
analysis with subsampled CMIP5 simulations.
Because the number of simulations which are
Figure 3. Estimated contribution by anthropogenic (red) andfound to have the observed ENSO phase simultanatural (green) forcing to global mean surface temperature
neously for all three eruptions is very small (3 out
anomalies from 1956 to 2005 using (a) all CMIP5 simulationsof 198 historical simulations, and 0 out of 68
and (b) only those simulations with the observed ENSO
historicalNat simulations), we split the time period
phase. Bold lines are the best ﬁt scaling factor; shading shows
the 5–95% uncertainty range.
into three equal 16 year segments with each segment containing one eruption. The time series foreach segment then consists of the mean across model simulations with the correct (e.g., observed) ENSO
phase (see Figure S2). To keep the number of simulations constant for each segment of the subsampled timeseries, we restrict the number of simulations to 35 randomly selected historical simulations and 13 randomly
selected historicalNat simulations, the smallest number of available selected simulations across the three
eruptions (see Table S1). The ﬁnal scaling factor ranges are calculated from the combined distribution from100 such random combinations of available simulations. Detailed analysis shows that splitting the time series
does not affect the estimated magnitude of the volcanic response (see supporting information section S4
and S5).
Results of the detection and attribution analysis using ﬁngerprints from subsampled simulations yield a scaling factor for natural forcing of about 0.75 with an uncertainty range that encompasses one (Figure 3b). This
indicates that the amplitude of the naturally forced response in models is no longer inconsistent with observations and the difference between the model mean and observed response can be explained by internal
climate variability.

4. Summary and Conclusion
In summary, the GMST response to volcanic eruptions is consistent, within uncertainties, between models
and observations when the effects of internal variability, in particular, the coincidence of El Niño events(be it by chance or due to an eruption triggering an El Niño event) is explicitly accounted for. This does
not rule out that there may be model errors in the response to volcanic eruptions, for example, due to errors
in the forcing time series itself [Schmidt et al., 2014], due to aerosol-related processes [Stoffel et al., 2015], or
due to the response of the atmospheric circulation to the warming of the stratosphere [Driscoll et al., 2012].
Our results have important implications for detection and attribution studies. If a phenomenon under study israre and the observational record short (as is the case for volcanic eruptions during the twentieth century),
potential superposition of forced and unforced variability needs to be taken carefully into account. These
results are also important for paleoclimate research, as potential superposition of eruptions and El Niño
events during the last millennium adds another source of uncertainty to paleoclimate model-data comparisons [Stoffel et al., 2015, and references therein].
Other aspects of the climate system besides GMST may also be confounded by the superposition of volcanic
eruptions and El Niño in the recent past, for example, the carbon cycle [Frölicher et al., 2013] and the Southern
Annular Mode, which are subject of ongoing research.

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