ARTICLE
Received 28 Nov 2016 | Accepted 5 Apr 2017 | Published 6 Jun 2017

DOI: 10.1038/ncomms15531

OPEN

Extreme temperatures in Southeast Asia caused
by El Niño and worsened by global warming
Kaustubh Thirumalai1, Pedro N. DiNezio1, Yuko Okumura1 & Clara Deser2

In April 2016, southeast Asia experienced surface air temperatures (SATs) that surpassed
national records, exacerbated energy consumption, disrupted agriculture and caused severe
human discomfort. Here we show using observations and an ensemble of global warming
simulations the combined impact of the El Niño/Southern Oscillation (ENSO) phenomenon
and long-term warming on regional SAT extremes. We ﬁnd a robust relationship between
ENSO and southeast Asian SATs wherein virtually all April extremes occur during El Niñoyears. We then quantify the relative contributions of long-term warming and the 2015–16
El Niño to the extreme April 2016 SATs. The results indicate that global warming increases
the likelihood of record-breaking April extremes where we estimate that 29% of the 2016
anomaly was caused by warming and 49% by El Niño. These post-Niño Aprils can potentiallybe anticipated a few months in advance, and thus, help societies prepare for the projected
continued increases in extremes.

1 Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, J. J. Pickle Research Campus, Building 196, 10100 Burnet Road

(R2200), Austin, Texas 78758, USA. 2 Climate Analysis Section, Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder,
Colorado 80305, USA. Correspondence and requests for materials should be addressed to K.T. (email: kau@ig.utexas.edu).
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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15531

Mainland southeast Asia (MSA) encountered its warmest
monthly mean surface air temperatures (SATs) in April
2016 since record-keeping began in the mid-twentiethcentury1–3. Apart from surpassing national temperature records
in MSA, this event disrupted crop production, imposed societal
distress and resulted in peak energy consumption4–6.
Investigating the causes of such an event is essential toanticipating future extremes, especially in light of ongoing andprojected long-term warming in the region7.
While it is unmistakable that the Earth is warming globally due
to the effect of increasing greenhouse gases8, the impact ofwarming at spatial scales of MSA, including the causes ofextremes such as the April 2016 event, is more uncertain9. In the
MSA region, which encompasses Cambodia, Laos, Myanmar,
Thailand, Vietnam and peninsular Malaysia (Fig. 1a), April isparticularly prone to experiencing record-breaking SATs as it isclimatologically the warmest month of the year (Fig. 1b).
Studies using climate models suggest that tropical land areas,
particularly Central Africa, the Maritime Continent and theIndian Ocean rim, will show the most rapid and robust
intensiﬁcation of peak seasonal temperatures, with at least 60%
of regional land areas exceeding the late twentieth century
maximumaroundthe
mid-twenty-ﬁrst
century10–13.
Observations show that large parts of the Earth haveexperienced a signiﬁcant local shift towards warmer
temperatures in the summer season, particularly at lower
latitudes11–14, with one recent study15 indicating that MSA has
likely experienced the emergence of distinctly higher peakseasonal SATs in response to global warming since the year
2000. Perhaps the hot April of 2016 is an indication that the MSA
region is already experiencing a departure from its pre-industrial
climate, as predicted by the models? These studies indicate thaton the spatial scales of MSA, long-term warming is expected toemerge sooner in the lower latitudes relative to anywhere else on
the planet. This occurs because lower latitudes experiencenaturally lower year-to-year variability compared to higher
latitude continental regions, thus allowing for an earlier
detection of climate departures from a reference state12,16,17.
For this reason the Intergovernmental Panel on Climate Change
5th Assessment Report concluded with high conﬁdence that
relative to natural variability, near-term increases in seasonal andannual mean temperatures are expected to be larger in the tropics
and subtropics than in mid-latitude regions8.
In this work, we use observations as well as an ensemble ofglobal warming simulations to understand the relationshipbetween long-term warming and natural climate variability in
MSA, and their combined impact on extreme SATs. We
demonstrate that extreme April SATs in the region occur after
the peak of El Niño years and characterize the impact of longterm warming on these events. Finally, we quantify the relative
contributions of the El Niño/Southern Oscillation (ENSO) andlong-term warming in producing the April 2016 event anddemonstrate that with the continued march of ongoing global
warming, record-breaking SATs in MSA will occur more
frequently in the future.
Results
El Niño and April SATs in MSA. Climate in the MSA region
exhibits pronounced year-to-year variability that is strongly
linked to the ENSO phenomenon7,18. April SATs over MSA are
highly correlated (r ¼ 0.73) with the Niño-3.4 sea-surface
temperature (SST) index, a common metric used to monitor
ENSO variability19 that peaks during the December–January–
February (DJF) season (Fig. 1). Positive DJF Niño-3.4 SST
anomalies are associated with El Niño events and correspond to2

positive April SAT anomalies in MSA (Fig. 1d; hereafter‘post-Niño Aprils’; see also Supplementary Fig. 2). Whilestudies have suggested that this link with ENSO can be
exploited to anticipate SAT anomalies after peak SSTs during
the DJF season14,18,20, its impact on extremes in MSA during
April remains poorly understand, especially in light of ongoing
long-term warming9.
We explored the MSA April-El-Niño link further through acomposite analysis of observed SATs during post-Niño Aprils.
SAT anomalies from the GISTEMP2 and CRU3 (land) and
HadISST21 (oceans) data sets show that, on average, post-NiñoAprils are characterized by warming over MSA (Fig. 2a,c), withenhanced warming over land. The April temperature response in
the region to El Niño is among the largest in the world. Thispattern is robust across the observational data sets, which show
that, on average, post-Niño Aprils are accompanied by 0.6–0.7 °C
of warming in MSA (Fig. 2a,b). A similar pattern of land
ampliﬁcation of the post-Niño April heat is seen in an ensembleof simulations of twentieth and twenty-ﬁrst century climate
performed with the Community Earth System Model Version 1
Large Ensemble (CESM1-LE) (Fig. 2e) and the CMIP5 suite ofmodel members (Fig. 2g), conﬁrming the observed MSA April-ElNiño link22,23.
These composite analyses reveal that peak seasonal SATs in the
MSA region are highly sensitive to El Niño. Previous studies
proposed that changes in atmospheric circulation associated with
El Niño reduce cloud cover over the land areas of the Maritime
Continent and Indian Ocean rim causing the associatedwarming20,24–26. To test whether clouds play a role causing
extremes SATs over MSA, we performed a composite analysis ofcloud cover anomalies during post-Niño Aprils using land- andship-based observations together with reanalysis data as well as
CESM1-LE and CMIP5 output (Fig. 2). We ﬁnd that cloud coverobservations from the ERA-interim data set27 and CRU (land)
and ICOADS (ocean) data sets3,28 show, on average, reduced
cloud cover over MSA during post-Niño Aprils (Fig. 2b,d). These
reductions in cloud cover appear to be linked with increased
cloud cover over the central Paciﬁc, suggesting that the SST
anomalies associated with decaying El Niño events could be
driving anomalous overturning circulation in the atmosphere andthereby creating drought conditions over southeast Asia
(Supplementary Figs 2 and 3; ref. 20). Our analysis shows thatduring April, the area of reduced rainfall and cloud cover has
moved from the Maritime Continent, where El Niño impacts are
more pronounced at their winter peak, to over MSA and thePhilippines, explaining the region’s sensitivity to ENSO.
The CESM1-LE and CMIP5 ensemble means simulate
remarkably realistic patterns of surface temperature (Fig. 2e,g)
and cloud cover anomalies (Fig. 2f,h), compared with observations from different data sets. This lends further support for arobust relationship between El Niño and hot April extremes in
MSA inferred from observations. The CESM1-LE also simulates
ENSO realistically (DiNezio et al. in prep) including the tropical
SST patterns as well as circulation anomalies and teleconnections(see Methods for details on these simulations). The realism of
CESM1 allows us to use data from the Large Ensemble, consistingof output from 39 simulations of twentieth and twenty-ﬁrst
century climate22, to explore the effect of long-term
anthropogenic warming as well as El Niño on extreme seasonaltemperatures over MSA.
Histogram analysis of April SATs. Do all El Niño events lead tohotter Aprils over MSA? We address this question by analysing theeffect of El Niño and La Niña ( El Niño ’s cooler counterpart) on
the distribution of April SATs. In both observations and

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ab

April 2016
Land surface temperature anomaly

29
20 °N
Mainland Southeast Asia
surface air temperature (°C)

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10 °N

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MODIS instrument on
Terra and Aqua Satellites

110 °E
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CRU

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DJF anomaly (°C)

dMainland Southeast Asia
April SAT anomaly (°C)

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1990

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2020

Figure 1 | Surface temperatures over Mainland Southeast Asia. (a) Satellite-derived (MODIS instrument on Terra and Aqua satellites) land surface
temperature anomaly during April 2016 in the Mainland Southeast Asia (MSA) region (base period: 2000–2016), where the dashed red box represents theregion selected for our analysis. (b) Surface air temperature (SAT) climatology in MSA based on the entire CRU data set (1901–2014), which indicates that
April is the warmest month in the region. (c) April SAT anomalies in the MSA region from the GISTEMP (green squares) and CRU data sets (yellow
triangles) spanning from 1940 to present (base period: 1951–1980; ref. 34). MODIS-based land surface temperature anomalies for MSA (purple stars) arealso plotted with the reference base period adjusted to equal the April average of the other two data sets (d) The December–January–February (DJF)
anomaly of sea-surface temperatures (SST) in the Niño-3.4 region, Central Paciﬁc Ocean, where SSTs are taken from the HadISST1.1 data set. As an
indicator of El Niño events, a dashed red line is plotted at 0.5 °C. Shaded red bars connect El Niño events to hot Aprils in MSA.

simulations, after linearly detrending the data sets over their entire
length to remove the long-term warming trend, we divided all
April monthly mean SATs into distributions of post-El Niño andpost-La Niña Aprils. The resulting distributions show that indeed
the tails of the April MSA SATs are linked to ENSO variability.
Both observations and model data show that post-El Niño Aprils
are on average hotter (Fig. 3a–c, red bars) than post-La Niña Aprils(Fig. 3a–c, blue bars). Moreover, virtually all hot Aprils occurs
following El Niño events (80% for GISTEMP, B73% for CRU, andB88% for CESM1-LE post-Niño April extremes had 429 °C
detrended SAT values). The fact that the simulations accurately
capture this shift in the distribution is indicative that the effect of
ENSO on extreme SATs in MSA is robust.

Next, we looked into the effect of long-term regional warmingon the observed and simulated distributions of all April SATs in
MSA. Here, we divided the data sets into two equal subperiods anddetrended the data over each subperiod (GISTEMP: 1940–1977
and 1978–2016; CRU: 1901–1958 and 1959–2015; CESM1-LE:
1920–1968 and 1969–2015). This ensures that the mean warming
between the individual time periods is retained for each data set
despite differing time periods (see also Supplementary Figs 4 and
5). The long-term warming results in distributions of April SATs
with signiﬁcant positive shifts in the mean according to a twosample t-test (Po0.01). The distribution of SATs shifts due to thischange in the mean and results in an increase of extreme SATs in
the latter subperiod.

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GISTEMP (1940–2016)

0.5
0CRU+HadlSST (1900–2014)0.5
Surface temperature anomaly (°C)

ERA-Interim (1979–2014)

30°N

10

20°N

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–10

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c

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CRU+COADS (1900–2014)
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20°N

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30°N

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Cloud cover anomaly (%)

a

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CMIP5 (1850–2015)

0.5
0

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CMIP5 (1850–2015)

30°N

5

20°N

0

0

–5

–0.5
90°E

120°E 150°E 180°W 150°W

90°E

120°E 150°E 180°W 150°W

Figure 2 | Post-Niño April composites. Composites of (a–d) observed and (e–h) simulated (left) SAT and (right) cloud cover anomalies during April after
El Niño events. The SAT observations are from the (a) GISTEMP datas et (1940–2016) and (c) CRU and HadISST1.1 data sets (1900–2014) whereas thecloud cover observations are from the (b) ERA-interim reanalysis data set and (d) the CRU and ICOADS data sets. The simulated composites are from(c) the CESM1-LE model with data from 1920 to 2015 (ref. 22) and the CMIP5 Ensemble from 1850 to 2015 (ref. 23; stippling indicates agreement between
majority of the ensemble members). Composites were produced by averaging April anomalies after the peak of El Niño events where the simulated composites
are the average composite among the 39 CESM1-LE members. El Niño events were identiﬁed as those years with peaks of the Niño-3.4 SST index larger than
0.5 standard deviations in each (observational and simulated) data set. The CESM1 composite SAT anomalies are scaled down by a factor of 0.45 over bothland and ocean so that the April composite SSTanomalies averaged over the Niño-3.4 region agree in magnitude with the observed April HadISST1.1 composite.
No scaling was performed for the cloud cover data simulated by CESM nor for the SAT and cloud cover simulations from the CMIP5 ensemble.

Lastly, we performed a similar analysis of only post-NiñoAprils (Fig. 3c,f,i). We ﬁnd that the latter subperiods show more
extreme post-Niño April SATs because of a signiﬁcant shift in themean of the distribution (two-sample t-test; Po0.01) but
statistically indistinguishable variance (f-test; P40.05). Changes
in the tails of the distribution caused by shifts in the mean, similar
to the ones identiﬁed here, are the most straightforward way inwhich global warming can lead to increased frequency of
extremes16,29. Although increased extremes can also arise fromincreased variability, we do not observe statisticallydistinguishable variance between the two subperiods across anyof these distributions (f-test; P40.05). Thus, the mean shift in
post-Niño April SATs underpins long-term warming as the most
likely cause of the increasing extremes in MSA.
Attribution of extreme April SATs. Based on this assessment of
the roles of El Niño and long-term warming in modulating April
SATs, we built a statistical model to attribute the 2016 event andother April extremes in MSA. The model is a simple multiple
regression in the form SATApril ¼ at þ bt2 þ gT þ d, where April
SATs in MSA (SATApril) are regressed upon linear and quadraticcomponents of the regional long-term warming trend18,30
represented by time in years (t and t2, respectively), a term
4

proportional to DJF Niño-3.4 SST Index (T) capturing variability
correlated with ENSO and a residual term (d) capturing the effectof other climate variability and weather. This analysis yielded a ﬁtthat has a high correlation coefﬁcient (r ¼ 0.83) with Apriltemperature observations from 1940 to 2016 (Supplementary
Fig. 6), indicating that long-term warming and DJF ENSO
amplitude explain much of the SAT variability during April in
MSA. The unexplained residual between our regression ﬁt andthe observed anomaly (termed ‘other’) appears to be related to
mid-latitude weather over Asia or local weather (seeSupplementary Discussion). However, as the statistical model
explains a large component of April SATs, we used the regression
model to attribute the relative contributions of long-termwarming and ENSO in producing observed extremes in MSA.
We chose the 15 hottest Aprils (greater than the 80th percentileof the warmest Aprils) from the entire GISTEMP data set(1940–2016) and based on our regression model, investigated theinﬂuence of long-term warming versus El Niño in each April
extreme (Fig. 4). All 15 events occur after 1980, i.e., in the latter,
warmer subperiod of our previous analysis. April 2016 is thewarmest on record, surpassing the previous record holder, April
1998, by 0.9 °C (Fig. 1c). Both of these record-breaking Aprils
occurred after the peak of extremely strong El Niño events(1997–1998 and 2015–2016). The ENSO contribution typically

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CRU (1901–2014)

GISTEMP (1940–2016)
25

Post-EI-Niño
Post-La-Niña
All years

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CESM1-LE (1920–2015)

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1901–1957
1958–2014

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1940–1977
1978–2016

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Post-Niño Apriltemperature (°C)

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Post-Niño Apriltemperature (°C)

Figure 3 | Histograms of April SATs. SATs for Mainland Southeast Asia (MSA) are taken from the GISTEMP (1940–2016; a–c) and CRU (1901–2014; d–f)
observational data sets along with simulations from the CESM1-LE (1920–2015; g–i). Histograms in the top panel (a,d,g) delineate all the April data (grey)
into those occurring after El Niño (red) and La Niña (blue) events over the entire time period. Histograms in the middle panel (b,e,h) separate the available
April surface temperature data into two equal time periods (dark blue—earlier; orange—latter) over the length of the individual data sets. The bottom panel
histograms isolate post-Niño-April events in the observations (c,f) and simulations (i) over the time period indicated in the middle panel, i.e., they separate
the post-Niño events (red) in the topmost panel into the subperiods indicated by the middle panel for each of the data sets. All data sets were linearly
detrended prior to analyses where the top panel histograms (a,d,g) were detrended over the entire time period for each data set, whereas linear detrending
was performed for each subdivided time period in the subsequent analysis. We found that over their respective time periods for both GISTEMP (1940–
1977; 1978–2016) and CRU (1901–1958; 1959–2014) data sets, the mean was signiﬁcantly larger in the more recent subperiod (t-test: H ¼ Ha; Po0.01)
whereas the variance was not signiﬁcantly different between the subperiods (f-test: H ¼ Ho; P40.05).

accounts for 30–75% (0.25–1.15 °C) of the hot April SAT
anomaly in MSA during those hot April events coinciding with
El Niño years. Indeed, the eight hottest Aprils (490th percentile)
all coincided with El Niño years (labelled in red in Fig. 4a), with
the notable exception of April 2001 where the residual term
dominates. On the other hand, the recent hot Aprils of 2001, 2013
and 2014 do not occur during El Niño years, and as such, theiranomalies (albeit minor) are mainly explained by long-termwarming (Fig. 4). However, even though the regression modelcannot fully explain the observed anomalies for these outliers (seeresidual component labelled ‘other’ in Fig. 4a), it does indicatethat long-term warming is increasingly playing a role (B25–60%)
in producing extreme April SATs in the absence of El Niñoevents. The regression model also indicates an upward trend in
the relative contribution of long-term warming, which increases
across these 15 events until 2016, and will foreseeably increase as
regional warming continues to inﬂuence hot Aprils.
The regression model indicates that the long-term warming
trend caused B29% of the extreme 2016 April SAT in MSA(Fig. 4a). Despite its unprecedented strength, El Niño accounted
for B49% of the record-breaking hot April of 2016, leaving 22%
unexplained. If some amount of the unexplained portion of the

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aObservations

Regression-based contribution to hot
April SAT anomaly (°C)

2.5

Warming trend
El Niño
Other

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1940:1970
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Trendline

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April (years)

Simulations

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during next record-breaking April

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Figure 4 | Attributing hot Aprils in Mainland Southeast Asia.
(a) Observations: The relative contribution of El Niño (green bars) versus
the long-term warming trend (red bars) towards the 15 hottest April SATs
(480th percentile) in the GISTEMP record of Mainland Southeast Asia
(MSA; 1940–2016) using a regression model. The residual of the observed
anomaly and the regression ﬁt is termed as ‘other’ variability (yellow bars).
The years in red on the x-axis indicate the eight hottest extreme April
events (490th percentile). (b) Simulations: Median lead time for the next
record-breaking April (years) compared to the baseline period (colours inlegend) versus the median DJF Niño-3.4 SST anomaly (°C) for that
forthcoming record-breaking year across all CESM1-LE members (with ±1s
ranges across the 39 members at each baseline period). A trendline forthese datapoints calculated with the uncertainty is shown (black dashedline). Also depicted are the estimated probability density functions from thehistograms of lead times as well as the DJF Niño-3.4 SST anomalies during
the next record-breaking April at each baseline period.

2016 April SAT, apart from the contribution of weather-related
noise, arose from a nonlinear change in the rate of global
warming30, the imprint of regional warming on the 2016 extreme
would be even larger than our estimate. Regardless of thisunexplained portion, our regression model holds predictive
value18 as the DJF Niño-3.4 SST anomaly can be observed
months before the potential hot April.
Future temperature extremes in MSA. With continued warming
projected for the rest of this century8, will MSA experience more
6

frequent, record-breaking hot Aprils? We investigated thelikelihood of MSA extremes becoming more common in the
future by analysing SAT data from the CESM1-LE. Starting from1940 and using a non-overlapping moving window of 30 years,
we calculated the hottest April in each of the ensemble members
for that baseline period. For each of these record hot Aprils, we
also calculated the DJF Niño-3.4 SST anomaly of that year. We
then determined the lead time for the next, new, record-breaking
April across each member (i.e. difference in years between thebaseline record and new record). For each lead time, we also
computed the DJF Niño-3.4 SST anomaly for that year in order toexplore the control of El Niño on April extremes. Finally,
we repeated this procedure for windows from 1940–1970 to
2030–2060 to understand the effect of warming on the lead-time
for the next record-breaking April (Fig. 4b).
The median lead times across all ensemble members indicatethat indeed, with future warming, record-breaking April SATs
occur more frequently in MSA (Fig. 4b), a result in agreementwith the statistics of record-breaking events31. The simulations
suggest that record-breaking April SATs relative to an earlytwentieth century baseline occurred during large El Niño events(DJF Niño-3.4 SST anomaly 42 °C) whereas record-breaking
April events in the twenty-ﬁrst century can occur during El Niñoevents of smaller magnitudes. We note that 88.5% (414 out of468) of all record-breaking extremes occur during El Niño years(DJF Niño-3.4 SST anomaly 40.5 °C), where 63% (260 out of414) occur during strong events (DJF Niño-3.4 SST anomaly
42 °C). Thus, even though global warming will induce more
extreme SATs in the future for the MSA region, our ﬁndings areadvantageous for anticipating hot Aprils by monitoring the peakof Niño-3.4 SSTs a few months in advance.
Discussion
Understanding and projecting the impacts of global warming on
regional extremes is of critical importance for adaptation
purposes32,33. On the spatiotemporal scales addressed in thisstudy, the magnitude of natural variability is large relative tolong-term trends and makes the detection and attribution of
global warming more challenging. While located in the tropics,
MSA is an exception to the general rule that anthropogenic
warming will emerge sooner in the low latitudes, as shown by theprominent effect of ENSO on SAT variability in the region (Figs 1
and 2). The hot April of 2016 is an example of such ENSOmodulated extremes. Nevertheless, by focusing on post-El NiñoAprils (Fig. 3), and because the strong correlation with ENSO
allows for the removal of its effect, we were able to detect the
impact of long-term warming on observed record-breaking SATs
in MSA (Fig. 4a). Despite a low number of El Niño events in theobservational record, the CESM1-LE simulations provide
conclusive statistics that long-term warming is increasingly
aggravating the effect of El Niño in governing the frequency ofthese extremes (Fig. 4b). It is very likely that continued warmingin the region will continue to conspire with El Niño in order tobring more common record-breaking extremes in the future.
Even though the record-breaking April 2016 extreme in MSA wasprimed by the El Niño of 2015, compared to previous post-Niñoextremes, the inﬂuence of long-term warming is incontrovertible.
Furthermore, the inﬂuence of long-term warming on future El
Niño impacts will only rise in importance. These extreme events
could be quantitatively predicted some months in advance, andcan thereby increase the preparedness of societies that will be
impacted.
Methods
Surface temperature data sets. We used four different observational data sets foranalysing surface temperature in MSA: GISTEMP1,2, CRU Ver 3.23 (ref. 3),

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HadISST1.1 (ref. 21) and the satellite-based MODIS data set34. The majority of ouranalysis on MSA were performed using the GISTEMP data set spanning from 1940
to 2016 (Figs 1–4). 1940 was chosen as the start year due to the increasing densityof temperature stations in MSA1,2, which is also reﬂected in the improved
agreement between CRU and GISTEMP after 1940 (Supplementary Fig. 1).
Composite post-Niño April analysis in Fig. 2c was performed jointly on the CRU(land) and HadISST (ocean) data sets from 1900 to 2014. We used the ICOADS28,
ERA-Interim27 and HadSLP2 data sets35 to investigate cloud cover (Fig. 2b–d),
surface energy ﬂuxes (Supplementary Fig. 2) and sea-level pressure (Supplementary
Fig. 3). We also use temperature station data for Bangkok (Supplementary Fig. 4)
spanning from 1870 to 2013 taken from the Berkeley Earth Surface Temperature
data set36. Though this was available from 1813, we only focus on the post-1870
period because of the availability of HadISST1.1 data to compute the Niño-3.4 SST
index. Unless stated otherwise, we employed linear detrending to remove trends
throughout our study. For both observations and simulations, we used the same
spatial extent of the MSA box for analysis (6–22 °N, 94–110 °E).

Multivariate regression. We regressed April SATs in MSA upon linear andquadratic components of the long-term warming trend along with a term proportional to the DJF Niño-3.4 SST Index and a residual term in the form
SATApril ¼ at þ bt2 þ gT þ d where t is time in years, T is the DJF Niño-3.4 SST
anomaly and a,b, and g are the regression coefﬁcients. The ﬁt yielded a highcorrelation coefﬁcient (r ¼ 0.83; Supplementary Fig. 6), which indicates that ENSO
and global warming explain much of the April SAT variability. The analysis andresults were similar even when we used only the December Niño-3.4 SST anomaly(r ¼ 0.76), no quadratic long-term warming term and only a linear component(r ¼ 0.74), or global mean surface temperatures instead of April SATs in MSA(r ¼ 0.80). However, the regression conﬁguration used in the main text provided
the highest correlation coefﬁcient. We used this regression to attribute April
extremes in MSA to El Niño and long-term warming (Fig. 4a). The negative
contributions of El Niño, although minimal (o10%) for the three non-El-Niñoextremes (2001, 2013, and 2014), are not shown. The ‘other’ variability was calculated as the difference between the observed anomaly and the residual ﬁt (where
the negative El Niño contributions were set to zero where they applied).

Climate model simulations. We used model output from the Community Earth
System Model Version 1 (CESM1) which consists of a large ensemble (hereinafter
CESM1-LE) of 39 simulation of climate from 1920 to 2100 (ref. 22). The large
number of realizations allows us to perform a more robust statistical analysis of thelink between El Niño and extreme April SATs for this study, as well attributes thechanges to increasing anthropogenic forcings. All the simulations were run withhistorical forcings until the year 2005, after which the forcings follow the RCP8.5
scenario8. Each member is unique due to the application of a small, random
perturbation to the initial air temperature at 1920, which leads to independent
simulated trajectories in weather and internal climate variability among themembers. However, since external forcings are the same, all the members containthe same anthropogenic (forced) response22.
ENSO variability arises spontaneously in the CESM1-LE set of simulations
where many aspects of El Niño and La Nina events are realistically simulated,
although El Niño events show amplitudes that exceed those of observed events37.
For the composite analysis (Fig. 2c), El Niño events were selected from 1920 to2015 in each member based on the peak of SST anomalies in the Niño-3.4 region
that were larger than 0.5 standard deviations of the entire time series of eachensemble member. The composite plot was not sensitive to whether this threshold
was increased to 0.75 standard deviations or 1 standard deviation. Over this time
period, prior to picking El Niño events, we linearly detrended the Niño-3.4 SST
timeseries simulated in each member to separate ENSO from the long-termwarming trend. As stated in the main text, for plotting purposes, we downscaled
the CESM1-LE composite SAT values by 0.45 over land and the oceans so that the
April composite SST anomalies averaged over the Niño-3.4 region agree inmagnitude with the observed April HadISST1.1 composite.
We also performed similar analyses on a 10-member ensemble of simulations
from the Community Atmosphere Model Version 5 run in Tropical Ocean-Global
Atmosphere conﬁguration (CAM5-TOGA; Supplementary Fig. 5a). The CAM5TOGA simulations, which are designed to simulate atmospheric variability
uncoupled from the ocean, are run with observed SST ﬁelds in the tropics, whereby
ENSO events occur with the same timing and amplitude as in nature. Thus, as
opposed to the CESM1-LE, ENSO events are prescribed in these set of simulations.
However, similar to the CESM1-LE, each member is uniquely different also due tosmall perturbations in initial air temperature conditions, resulting in independent
weather trajectories. We ﬁnd that composite analysis of post-Niño Aprils as well as
histograms obtained from CAM5-TOGA agree with CESM1-LE and also with theobservations (cf. Figs 2 and 3 and Supplementary Fig. 5).
Finally, we used the CESM1-LE set of simulations for analysing the lead time
and the corresponding DJF Niño-3.4 SST anomaly for new record-breaking April
SAT extremes relative to a baseline period (Fig. 4b). For this analysis, we used a
non-overlapping sliding window from 1940 to 1970 until 2030 to 2060 and foundthat increasing/decreasing the baseline period by up to 10 years and/or allowing
overlap did not change our ﬁndings, i.e., lead times and magnitude of DJF Niño3.4SST anomalies required for future record-breaking Aprils decrease with the adventof long-term warming in MSA. We refrained from using the complete data set
from 1920 to 2100 due to potential edge effects affecting our analysis (e.g. as
detailed by Hawkins et al.16).
For the 1920–2060 time range, our ENSO deﬁnition is based on detrendedNiño-3.4 data to separate ENSO from global warming. Instead of directly
removing trends however, we removed the forced warming from the Niño3.4
timeseries, which we compute from the ensemble-mean Niño-3.4 timeseries.
Small interannual variability remains in the ensemble mean, which we smoothusing a 10-year low pass ﬁlter. Then we remove this estimate of the forced
warming from the Niño-3.4 index from each individual member to isolate theunforced variability. We chose El Niño (La Niña) events as those years where
peak SSTs were higher (lower) than 0.5 standard deviations of the overall
timeseries in each member after subtracting the 10-year smoothed ensemble
mean of Niño-3.4. Our results were not affected if we chose 0.5 standard
deviations or 1 standard deviation to pick ENSO events. The trend line in Fig. 4b(black dashed line) was calculated using a maximum likelihood regression
methodology that incorporates bivariate error38,39.

Code availability. The MATLAB codes that have contributed to the results andanalysis reported in this study are readily available upon request from the leadauthor (K.T.: kau@ig.utexas.edu; Git: holy-kau).
Data availability. The observational/reanalysis data sets used in this study areavailable here: the GISTEMP data set2—https://data.giss.nasa.gov/gistemp/, theCRU Ver 3.23 data set3—https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_3.23/, theHadISST1.1 data set21—http://www.metofﬁce.gov.uk/hadobs/hadisst/, the MODIS
data set34—https://lpdaac.usgs.gov/dataset_discovery/modis, the ERA-Interim
data set27—https://esrl.noaa.gov/psd/data/gridded/data.erainterim.html, theICOADS data set28—http://icoads.noaa.gov, the HadSLP2 data set35—http://
www.metofﬁce.gov.uk/hadobs/hadslp2/, the BEST data set36—http://
berkeleyearth.org. Output from the CESM large ensemble22 simulations can befound at http://www.cesm.ucar.edu/projects/community-projects/LENS/ and theCMIP5 (ref. 23) simulations at https://esgf-node.llnl.gov/.

References
1. Hansen, J., Ruedy, R., Sato, M. & Lo, K. Global surface temperature change.
Rev. Geophys. 48, RG4004 (2010).
2. GISTEMP Team and others. Giss Surface Temperature Analysis (gistemp)(NASA Goddard Institute for Space Studies, 2016). Retrieved 5/1/2016 fromhttp://data.giss.nasa.gov/gistemp/.
3. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution
grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J.
Climatol. 34, 623–642 (2013).
4. Electricity Generating Authority of Thailand. Electricity peak demand hits
28,351.7 MW, breaking the peak record for the second time in 2016.
http://www.egat.co.th/en/index.php?option=com_content&view=article&id=
326:electricity-peak-demand-hits-28-351-7-mw-breaking-the-peak-record-forthe-catid=11&Itemid=112 (2016).
5. Associated Press. Scorching heat wave in Thailand is longest in 65 years. http://
www.chicagotribune.com/news/nationworld/ct-thailand-hot-april-20160427story.html (2016).
6. Nirmal Ghosh. Heatwave In Asia: coffee crops die in Vietnam, Thai rice yield
shrinks. http://www.asianews.network/content/heatwave-asia-coffee-crops-dievietnam-thai-rice-yield-shrinks-15252 (2016).
7. Singhrattna, N., Rajagopalan, B. & Kumar, K. K. Interannual and interdecadal
variability of Thailand summer monsoon season. J. Climate 18, 1697–1708(2005).
8. Stocker, T. F. et al. Summary for policymakers. Climate Change 2013: thePhysical Science Basis Contribution of Working Group I to the Fifth Assessment
Report of the Intergovernmental Panel on Climate Change (Cambridge
University Press, 2013).
9. Manton, M. J. et al. Trends in extreme daily rainfall and temperature in
Southeast Asia and the South Paciﬁc: 1961–1998. Int. J. Climatol. 21, 269–284(2001).
10. Mahlstein, I., Knutti, R., Solomon, S. & Portmann, R. W. Early onset ofsigniﬁcant local warming in low latitude countries. Environ. Res. Lett. 6, 034009(2011).
11. Diffenbaugh, N. S. & Scherer, M. Observational and model evidence of global
emergence of permanent, unprecedented heat in the 20th and 21st centuries.
Climatic Change 107, 615–624 (2011).
12. Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res.
Lett. 39, L01702 (2012).
13. Lehner, F., Deser, C. & Sanderson, B. M. Future risk of record-breaking
summer temperatures and its mitigation. Climatic Change. doi:10.1007/s10584016-1616-2 (2016).
14. Herring, S. C., Hoerling, M. P. & Kossin, J. P. Explaining extreme events of2014 from a climate perspective. Bull. Am. Meteorol. Soc. 96, S1–S172 (2015).

NATURE COMMUNICATIONS | 8:15531 | DOI: 10.1038/ncomms15531 | www.nature.com/naturecommunications

7

ARTICLE

NATURE COMMUNICATIONS | DOI: 10.1038/ncomms15531

15. Mahlstein, I., Hegerl, G. & Solomon, S. Emerging local warming signals in
observational data. Geophys. Res. Lett. 39, L21711 (2012).
16. Hawkins, E. et al. Uncertainties in the timing of unprecedented climates.
Nature 511, E3–E5 (2014).
17. King, A. D. et al. The timing of anthropogenic emergence in simulated climate
extremes. Environ. Res. Lett. 10, 094015 (2015).
18. Nicholls, N. et al. The El Niño–Southern oscillation and daily temperature
extremes in east Asia and the west Paciﬁc. Geophys. Res. Lett. 32,
L16714–L16714 (2005).
19. Trenberth, K. E. The deﬁnition of El Niño. Bull. Am. Meteorol. Soc. 78,
2771–2777 (1997).
20. Trenberth, K. E., Caron, J. M., Stepaniak, D. P. & Worley, S. Evolution of El
Niño Southern Oscillation and global atmospheric surface temperatures.
J. Geophys. Res. 107, AAC 5–1–AAC 5–17 (2002).
21. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, andnight marine air temperature since the late nineteenth century. J. Geophys. Res.
108, 4407–4437 (2003).
22. Kay, J. E. et al. The Community Earth System Model (CESM) Large
Ensemble Project: a community resource for studying climate change in thepresence of internal climate variability. Bull. Am. Meteorol. Soc. 96,
1333–1349 (2015).
23. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and theexperiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
24. Halpert, M. S. & Ropelewski, C. F. Surface temperature patterns associated with
the Southern Oscillation. J. Climate 5, 577–593 (1992).
25. Trenberth, K. E. & Fasullo, J. T. Climate extremes and climate change: theRussian heat wave and other climate extremes of 2010. J. Geophys. Res. 117,
D17103 (2012).
26. Klein, S. A., Soden, B. J. & Lau, N.-C. Remote sea surface temperature variations
during ENSO: evidence for a tropical atmospheric bridge. J. Climate 12,917–932 (1999).
27. Dee, D. P. et al. The ERA-Interim reanalysis: conﬁguration and performanceof the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).
28. Worley, S. J., Woodruff, S. D., Reynolds, R. W., Lubker, S. J. & Lott, N. ICOADS
release 2.1 data and products. Int. J. Climatol. 25, 823–842 (2005).
29. Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heatwaves in the 21st century. Science 305, 994–997 (2004).
30. Meehl, G. A., Teng, H. & Arblaster, J. M. Climate model simulations of theobserved early-2000s hiatus of global warming. Nat. Climate Change 4,
898–902 (2014).
31. Benestad, R. E. Record-values, nonstationarity tests and extreme value
distributions. Global Planet. Change 44, 11–26 (2004).
32. Fedoroff, N. V. et al. Radically rethinking agriculture for the 21st century.
Science 327, 833–834 (2010).
33. Luo, J.-J. et al. Impacts of El Niño Southern Oscillation on the global yields ofmajor crops. Nat. Commun. 5, 1–7 (2014).
34. Wan, Z., Zhang, Y., Zhang, Q. & Li, Z. Validation of the land-surface
temperature products retrieved from Terra Moderate Resolution Imaging
Spectroradiometer data. Remote Sensing Environ. 83, 163–180 (2002).
35. Allan, R. & Ansell, T. A new globally complete monthly historical gridded
mean sea level pressure dataset (HadSLP2): 1850–2004. J. Climate 19, 5816–5842(2006).
36. Rohde, R. et al. Berkeley earth temperature averaging process. Geoinformatics &Geostatistics: an Overview 1, doi:10.4172/2327-4581.1000103 (2013).

8

37. DiNezio, P. N., Barbero, L., Long, M. C. & Lovenduski, N. Are anthropogenic
changes in the tropical ocean carbon cycle masked by Paciﬁc Decadal
Variability? US CLIVAR Variations 13, 12–23 (2015).
38. Thirumalai, K., Singh, A. & Ramesh, R. A MATLAB code to perform weighted
linear regression with (correlated or uncorrelated) errors in bivariate data. J.
Geol. Soc. India 77, 377–380 (2011).
39. York, D., Evensen, N. M., Martnez, M. L. & De Basabe Delgado, J. Uniﬁed
equations for the slope, intercept, and standard errors of the best straight line.
Am. J. Phys. 72, 367–369 (2004).

Acknowledgements
We thank Christina Karamperidou, Judson Partin, Timothy Shanahan and Claudia
Tebaldi for their comments on this work. K.T. is supported by the UTIG Postdoctoral
Fellowship. P.N.D. and Y.O. are supported by NOAA’s Climate Program Ofﬁce’s
Modeling, Analysis, Predictions, and Projections program (P.N.D.—NA14OAR4310229;
Y.O.—NA14OAR4310227). Y.O. is also supported by the NSF Climate and Large-Scale
Dynamics Program (AGS-1302346). Computing resources were provided by the Climate
Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory(CISL), sponsored by the National Science Foundation and other agencies. This article is
UTIG Contribution #3114.

Author contributions
K.T. and P.N.D. conceived and designed the study. K.T., P.N.D., Y.O. and C.D. together
discussed and contributed to the interpretation of data and ideas involved in this work,and ultimately to the conclusions of this research. K.T., along with input from P.N.D.,
performed the analyses, generated the ﬁgures and wrote the manuscript for this study.

Additional information
Supplementary Information accompanies this paper at http://www.nature.com/
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How to cite this article: Thirumalai, K. et al. Extreme temperatures in Southeast Asia
caused by El Niño and worsened by global warming. Nat. Commun. 8, 15531.
doi: 10.1038/ncomms15531 (2017).
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