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How Well Do We Know ENSO’s Climate Impacts over North America,and How Do We Evaluate Models Accordingly?
CLARA DESER, ISLA R. SIMPSON, ADAM. S. PHILLIPS, AND KAREN A. MCKINNONNational Center for Atmospheric Research, Boulder, Colorado(Manuscript received 15 November 2017, in final form 14 February 2018)
ABSTRACTThe role of sampling variability in ENSO composites of winter surface air temperature and precipitation
over North America during the period 1920–2013 is assessed for observations and ensembles of coupledmodel simulations in which sea surface temperature anomalies in the tropical eastern Pacific are nudged tothose of the real world. The individual members of each model ensemble show a surprising amount of diversity in their ENSO composites, despite being constructed from the same observed set of 18 El Niño and 14
La Niña events. For a given model, this ensemble spread can only be due to sampling variability, that is,aliasing of internal variability that is unrelated to ENSO, which in turn is shown to arise from internal atmospheric dynamics rather than coupled ocean–atmosphere processes. Analogous ensemble spread is evident
in 2000 synthetic ENSO composites based on observations using random sampling techniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced ENSO signal in the observational record. In some locations, the amplitude ofthe estimated ENSO signal can vary by more than a factor of two. This observational uncertainty necessitates
an approach to model assessment that considers not only the model’s forced response to ENSO, given by its
ensemble-mean ENSO composite, but also its representation of internal variability unrelated to ENSO. Suchan approach is used to reveal fidelities and shortcomings in the Community Earth System Model, version 1.

1. Introduction
The largest known source of seasonal climate forecast
skill over North America is El Niño – Southern Oscillation (ENSO), the leading mode of interannual variability of the tropical ocean–atmosphere system (Shukla
et al. 2000; Tippett et al. 2012; L’Heureux et al. 2015).
ENSO affects North American climate through changes
in the large-scale atmospheric circulation driven byanomalous deep convection and associated latent heatrelease in the tropical Indo-Pacific (e.g., Bjerknes 1969;
Horel and Wallace 1981; Held et al. 1989). These influences generally maximize in boreal winter and early
spring when atmospheric conditions are favorable for
Rossby wave propagation from the tropics to theNorthern Hemisphere and when the SST anomalies inthe tropical Pacific are the largest (Ropelewski and
Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JCLI-D-170783.s1.
Corresponding author: Dr. Clara Deser, cdeser@ucar.edu

Halpert 1986; Larkin and Harrison 2005; Chiodi andHarrison 2013; L’Heureux et al. 2015; and many others).
During ENSO’s positive phase (El Niño), anomalous
southerly winds advect warmer air over Alaska andCanada while anomalous northerlies bring cooler air tothe southeastern United States, and a strengthened andsouthward-shifted storm track brings above-normal
precipitation to the southern tier of the United Statesand drier conditions to the Ohio and upper Mississippi
River valleys. Opposite conditions tend to prevail during ENSO’s negative phase (La Niña). While the impacts described above are typical of ENSO events, they
do not necessarily occur during every episode. A recent
case in point is the El Niño of 2015/16, which failed tobring anticipated and much-needed rains to Southern
California and the U.S. Southwest desert, despite thefact that it was as strong as the El Niño events of 1982/83
and 1997/98, which did bring copious amounts of precipitation to this region. Similarly, the strong El Niño
events of 1991/92 and 1987/88 also lacked many of the
expected impacts over North America, as did the strong
La Niña events of 1973/74 and 2007/08 (www.climate.
gov). These counterexamples are consistent with the

DOI: 10.1175/JCLI-D-17-0783.1
Ó 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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fact that ENSO generally accounts for ,25% of thevariance of winter and spring climate anomalies over
North America, despite it being a dominant source ofpredictability (not shown, but see www.climate.gov).
The canonical impacts of ENSO may be obscured
during any given El Niño or La Niña event by competing influences from other sources of natural climate
variability. In addition, differences in the characterof each event (e.g., Rasmusson and Carpenter 1982;
Deser and Wallace 1987; Capotondi et al. 2015;
Takahashi and Martínez 2018) may affect their atmospheric teleconnections and associated climate impacts over North America (e.g., Garfinkel et al. 2013;
Johnson and Kosaka 2016). These issues motivate thequestion: how well do we know ENSO’s canonical influence on North American climate? Empirical studies
typically attempt to isolate ENSO-forced signals bycompositing over a large number of events or by applying regression analysis to a long period of record.
With a sufficiently long dataset (i.e., a large enoughsample of ENSO events), the noise due to variability
that exists in the absence of ENSO will be minimized,
revealing the true forced response. To what extent is
the observational record adequate to identify theforced response to ENSO without significant aliasing of
unrelated variability?
While empirical studies based on composite or regression analysis almost always include an assessment ofstatistical significance on the estimated ENSO signals,
this information does not necessarily convey the magnitude of the uncertainty at each location, nor does it
convey the spatial pattern of the uncertainty. For example, if the uncertainty arises from large-scale atmospheric variability, then the ‘‘noise’’ imparted toobservationally derived ENSO signals will also becharacterized by large-scale spatial patterns. In view ofthese issues, Deser et al. (2017, hereafter D17) proposed
an approach that integrates information on both pattern
and amplitude uncertainty that accompanies any empirical estimate of ENSO response based upon limited
data. In addition, they showed the utility of this integrated perspective when evaluating the realism of
ENSO signals in climate models, which have the luxury
of much larger samples sizes. The focus of D17 was on
the NH atmospheric circulation response to ENSO in
boreal winter. Here we extend this approach to investigate the surface air temperature (SAT) and precipitation (PR) responses to ENSO over North
America. To be consistent with D17, we use the same
period of record (1920–2013) and random samplingtechnique (with replacement) to construct synthetic
ENSO composites, each of which could have plausibly
happened had a different temporal sequence of natural

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variability, unrelated to ENSO, occurred. These synthetic ENSO composites provide important context for,and uncertainty bounds on, the one composite that actually occurred. Issues related to ENSO diversity andnonlinearity within these synthetic composites are also
addressed.
We also evaluate ENSO composites of SAT and
PR over North America from a multimodel ensembleof coupled simulations (the same used in D17) whose
tropical eastern Pacific sea surface temperature anomalies (SSTA) are nudged to observations during 1920–
2013. Each model provides an ensemble of simulations
starting from slightly different initial conditions. For
each, we construct an ENSO composite using the same
set of events as in our observational analysis. The resulting range of composites across the individual
members of a given model ensemble provides a direct
assessment of the uncertainties associated with any
single composite sample (i.e., the model’s ensemble
spread is the counterpart of the spread across the observationally based synthetic composites, for which
there is only one actual composite sample). That is,each composite within a given model ensemble represents an estimate of the model’s true forced response
to ENSO combined with a different sampling of its(unrelated) internal variability (throughout this paper,
we shall use the terms ‘‘internal’’ and ‘‘natural’’ interchangeably). Using the uncertainties derived for theobserved ENSO composite, we discriminate between
true biases in the models’ forced response to ENSO
and apparent biases that arise from limited sampling of
non-ENSO-related natural variability. In this way, we
also evaluate whether the spread across the ensemble
members of a given model is realistic. We focus on
December–February (DJF), the season when ENSO
generally has its largest impact over much of North
America. We also briefly show results for February–
April (FMA).
The rest of this study is organized as follows. The
observational datasets, model simulations, and methodology are described in section 2. Results are presented
in section 3, beginning with observed and simulated
ENSO composites of temperature and precipitation(section 3a), evaluation of the forced ENSO response
and internal variability in Community Earth System
Model, version 1 (CESM1; section 3b), the range of
observationally based synthetic ENSO composites(section 3c), comparison of ENSO composites acrossmodels (section 3d), the contributions of ENSO nonlinearity (section 3e) and ENSO diversity (section 3f) to
the synthetic observational composites, and ending withobserved ENSO composites for late winter (section 3g).
Discussion and summary are provided in section 4.

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2. Data and methods
a. Observational data
We use SAT from Berkeley Earth Surface Temperature (BEST; Rohde et al. 2013) and PR from Global
Precipitation Climatology Centre (GPCC), version 7(Schneider et al. 2014), both on a 18 latitude 3 18 longitude grid. In addition, we make use of sea level pressure (SLP) and PR from the Twentieth Century
Reanalysis (20CR), version 2c (Compo et al. 2011), on a
28 latitude 3 28 longitude grid, PR from ERA-20C (Poliet al. 2016) on a 1.58 latitude 3 1.58 longitude grid, andPR from the Global Precipitation Climatology Project(GPCP), version 2.3 (Adler et al. 2003), on a 2.58
latitude 3 2.58 longitude grid. Our analysis is based on
the period 1920–2013, except for GPCP, which is basedon the years 1979–2013.

b. Model simulations
We use the same model simulations as D17, which arebriefly summarized here; additional information is provided in D17. First, we make use of a coordinated set of
tropical Pacific pacemaker experiments (referred to as
PACE) performed with three state-of-the-art coupledclimate models: CESM1; Climate Model, version 2.1(CM2.1); and MIROC5. These experiments follow the
protocol of Kosaka and Xie (2013), in which monthly
SSTAs in the eastern tropical Pacific (108S–108N, 1608–908W,
with a linearly tapering buffer zone of 108 in latitude
and 208 in longitude) are nudged with a 2-day damping
time scale to those from the NOAA Extended Reconstruction Sea Surface Temperature, version 3b(ERSSTv3b), dataset; note that the SST mean state ineach model is maintained. An ensemble of experiments
was conducted with each model (10 for CESM1 andCM2.1, and 5 for MIROC5), produced by randomly
perturbing the initial atmospheric temperatures ineach member by a small (order 10214 K) amount. These
coupled model simulations arguably provide the most
realistic setting for evaluating the models’ ENSO composites, since only SSTAs in the eastern tropical Pacificare nudged to observations, leaving the rest of the global
climate system free to respond in an appropriately coupled manner (e.g., with two-way ocean–atmosphere interaction; Alexander et al. 2002). Our analysis is based on
the 1920–2013 period common to each model.
We also make use of a companion 10-member ensemble conducted with the atmosphere–land configuration of CESM1 in which the observed SST evolution is
specified throughout the tropics (within 288 latitude
and a linearly tapering buffer zone to 358 latitude) andthe observed SST climatological seasonal cycle is prescribed elsewhere [the so-called Tropical Ocean and

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Global Atmosphere (TOGA) protocol]. While thesesimulations have the advantage of a realistic tropic-wide
distribution of SST anomalies during ENSO (and a realistic SST mean state), they are more idealized than the
PACE experiments in the sense that they lack two-way
ocean–atmosphere coupling and extratropical SST variability. All simulations (PACE and TOGA) include thehistorical (and RCP8.5 after 2005) radiative forcing
protocols of CMIP5 (Taylor et al. 2012).
Last, we make use of a 2600-yr preindustrial control
simulation (1850 radiative conditions) of the atmosphere–
land configuration of CESM1 with a prescribed repeating
seasonal cycle of SSTs and sea ice conditions taken fromthe long-term climatology of a companion 2200-yr preindustrial control run of the fully coupled CESM1 (seeKay et al. 2015). This lengthy ‘‘atmosphere only’’ control
simulation provides robust statistics on the simulated levelof atmospheric circulation variability that exists in theabsence of ENSO and other SSTA forcing.

c. Methods
We use the same methodology as D17, of which asummary is given below. We compute monthly anomalies by subtracting the long-term monthly means basedon the period 1920–2013 from the corresponding monthof each year. We then form DJF and FMA averages
from the monthly anomalies and linearly detrend eachseasonal time series to reduce potential effects fromsecular climate change. Following D17, we evaluate
statistical significance using a two-sided Student’s t testat the 10% significance level as well as a random sampling approach discussed below. We identify 18 El Niño
(EN) and 14 La Niña (LN) events during 1920–2013
according to the criterion that the observed detrended
DJF Niño-3.4 (58S–58N, 1208–1708W) SST index exceeds
1 standard deviation (s) or falls below 21 s, respectively [using November–January (NDJ) in place of
DJF does not change the event selection]. We form
ENSO composites by subtracting the average of the 14
LN events from the average of the 18 EN events. Unless
noted otherwise, all results are based on DJF.
To evaluate the influence of sampling variability, we
form 2000 synthetic ENSO composites for observations
and for each model simulation by randomly samplingwith replacement from among the 18 EN events and the
14 LN events, always retaining 18 samples for the former
and 14 samples for the latter (these samples will necessarily omit some events and repeat others). As shown in
D17, the majority of these ‘‘bootstrapped’’ ENSO composites consist of 11–12 unique EN events and nine unique
LN events and a maximum repetition of three eventsof either sign. We also form 2000 synthetic ENSO composites for each model by drawing from all ensemble

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FIG. 1. ENSO composites of DJF SAT (8C) from each of the 10 CESM1 PACE simulations (labeled 1 through 10) and from (lower right)
observations. Each composite is based on the same set of 18 El Niño events minus 14 La Niña events during 1920–2013. Values not
significant at the 10% confidence level based on a two-sided t test are stippled.

members simultaneously (resulting in a larger number of
unique events and lower repetition rate).

3. Resultsa. Observed and simulated ENSO composites
The observed ENSO SAT composite shows a statistically significant dipole pattern of positive anomalies
over much of Canada and Alaska (maximum values;48C) and negative anomalies over the southeastern
United States (maximum amplitudes ;28C; Fig. 1,
lower right panel). This pattern is evident to some degree in all of the CESM1 PACE simulations, although
the magnitudes and exact locations of significant SAT

anomalies vary considerably (Fig. 1). For example, thewarming over western Canada and Alaska is nearly
twice as large in simulation 8 compared to simulation 5,
and the cooling over the United States is located in theSoutheast (as observed) in simulation 1 and over theSouthwest in simulation 8. Notably, simulations 2 and 4
show weak (amplitudes ,1.58C) and generally insignificant SAT anomalies throughout North America.
A similar level of diversity in ENSO SAT composites isapparent in the GFDL and MIROC PACE (Figs. S1and S2 in the online supplemental material) andCESM1 TOGA (Fig. S3) ensembles, although the lattershows generally larger amplitudes compared to its
PACE counterpart. Recall that each simulated composite is based on the same set of ENSO events as the1 JULY 2018

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FIG. 2. As in Fig. 1, but for PR (mm day21).

observed composite, highlighting the role of sampling
uncertainty.
The observed ENSO PR composite shows significant
positive anomalies over the southernmost U.S. statesand significant negative anomalies over southern
Ontario, Quebec, and the Ohio Valley–Upper South(the region encompassing Ohio, Michigan, Indiana,
Kentucky, Tennessee, and West Virginia), as well asinterior British Columbia, Alberta, western Manitoba,
and parts of the U.S. Northwest (Fig. 2k). The CESM1PACE ensemble generally reproduces this observed
pattern, but the amplitude and statistical significance ofthe regional PR anomalies vary across the members. For
example, California rainfall anomalies are considerably
weaker and more in line with observations in members
2, 5, and 10 compared to members 1, 4, 6, and 8. Similarly, the spatial extent, amplitude, and significance ofdrying over western Canada range considerably acrossthe simulations (note the contrast between members 2
and 8), as well as over the Ohio Valley–Upper South(note the absence of drying in members 4, 9, and 10). A
similar level of diversity in ENSO PR composites isevident in the other models and the CESM1 TOGA
ensemble (Figs. S4–S6).
These results highlight that a sample size of 18 EN
and 14 LN events may be insufficient to accurately
determine the ENSO-forced SAT and PR responses inmodels because of the presence of unrelated internalvariability that may obscure the ENSO signal. They
also raise the related issue of how well the observed
response to ENSO is known, even with 94 years of data
(1920–2013). Finally, the results underscore the challenge of evaluating the ENSO response in models given
sampling uncertainty in both the observational target

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FIG. 3. (top row) ENSO composites of DJF SAT (8C) for (a) ensemble mean of the 10 CESM1 PACE simulations, (b) observations, and(c) their difference. Stippling in (a) and (b) indicates values not significant at the 10% confidence level based on a two-sided t test. Stipplingin (c) indicates that the observed value lies outside of the 5%–95% values from the model’s 2000 bootstrapped composites, and red
contours (which occur only over Baja California) indicate the observed value lies outside any of the model’s bootstrapped composites.
(middle row) 5%–95% CIs (8C) on the SAT ENSO composites based on 2000 bootstrapped samples for (d) CESM1 PACE simulations,(e) observations, and (f) their difference. Stippling in (f) indicates regions where the observed value falls within the spread of values fromthe individual simulations. (bottom row) As in middle row, but for (g) the CAM5 atmospheric control simulation and (h) observations
after removing ENSO. (i) As in (c), but applying the observed CIs to the model simulations. See text for details.

and in each model simulation. We address theseissues next.

b. Evaluating CESM1’s response to ENSO
1) TEMPERATURE
The ensemble mean of the 10 CESM1 PACE ENSO
composites provides a robust estimate of the model’strue response to ENSO, as it is based on a total of 180
EN events and 140 LN events. Accordingly, theensemble-mean SAT response is significant over most of
the continent, with warming in Canada and Alaska(maximum values .38C in the Yukon and Alaska) andweaker-amplitude cooling over most of the contiguousUnited States (Fig. 3a). The pattern and amplitude ofthe ensemble-mean response is generally similar to theobserved composite except that the simulated warming
does not penetrate as far southeastward into the central
Canadian provinces and the north-central United States(cf. Figs. 3a and 3b). (Note however that individual realizations of the model, most notably simulations 1 and 8,
do show a more southerly extension than the ensemble
mean; recall Fig. 1). Differencing the observed composite
from the ensemble-mean composite reveals a significant
cold bias in the model’s ENSO response in the central
Canadian provinces and U.S. Upper Midwest; all otherregions show insignificant differences (Fig. 3c). Here,differences are deemed statistically significant if thevalue of the observed composite is lower than the 5th
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2000 model bootstrapped ENSO composite values
obtained by randomly sampling from among all 10
PACE ensemble members.
To properly evaluate whether the model has a realistic
forced response to ENSO, one must also assess its internal variability. This is because a bias in the model’s
forced response might not be detected if the simulated
internal variability is overestimated (i.e., the spreadamong ENSO composites is too large), or it might befalsely detected if the simulated internal variability isunderestimated (i.e., the spread among ENSO composites is too small). Here we assess the model’s internalvariability by comparing the spread between the 5th and95th percentiles of the 2000 bootstrapped ENSO composites for observations and CESM1 PACE (the lattercomputed using all ensemble members).
The spatial distribution of the model’s confidence intervals (CIs) (Fig. 3d) is similar to observations (Fig. 3e),with the largest values extending southeastward from
Alaska toward the U.S. Great Lakes. However, theiramplitudes exceed the observed values in western
Alaska, the far western United States, and the eastern
third of the continent by up to 18–28C (nonstippled regions in Fig. 3f). In these regions, the observed CI is lessthan the minimum CI of any of the 10 PACE runs, indicating that the model has enhanced variability compared to the real world (the CIs for each PACE
simulation are shown in Fig. S7). The larger CIs in themodel might obscure a true bias in the model’s forced
response. To address this possibility, we apply the observed CIs to the model’s ensemble-mean ENSO SAT
composite and reevaluate the significance on the difference between the SAT values from the ensemblemean composite and the observed composite (Fig. 3i).
The area with statistically significant differences expands slightly to encompass the southern Great Lakes
region compared to using the model’s CIs (cf. Figs. 3cand 3i). This additional region is thus an area where
there is a true bias in the model’s forced ENSO response, which had been obscured by the model’s overestimated CI. The model’s underestimated CIs over
far northeastern Canada do not affect assessment of
its forced ENSO response (cf. Figs. 3c and 3i). The
observed CIs will also be subject to uncertainty, in
analogy with the range of CIs across the individual
members of CESM1 PACE, but this has not been
investigated here.
Next, we make use of the 2600-yr atmospheric control
simulation of CESM1 to assess the contribution of internal atmospheric variability to the CIs in CESM1
PACE. To do so, we randomly select two groups of years(with replacement) from the control run, one consistingof 18 winters and the other of 14 winters. We then4997

average the SAT anomaly fields within each group andtake their difference, in analogy with how we formed the
ENSO composites. We repeat this procedure 2000
times, and use these 2000 random samples to compute
the CIs. The CIs from the atmosphere-only control run(Fig. 3g) are very similar to those from the coupledmodel’s PACE simulations (Fig. 3d), indicating thatinternal atmospheric variability accounts for most of theuncertainty obtained from the model’s bootstrapped
ENSO composites. This means that the spread in ENSO
SAT composites across the members of the CESM1PACE ensemble is primarily due to the superposition of
random (i.e., inherently unpredictable on interannual
time scales) internal atmospheric circulation anomalieson the forced ENSO response.
While we cannot isolate the contribution of internal
atmospheric dynamics to the observed CIs, we canevaluate the contribution of non-ENSO-related SAT
variability. To do this, we compute CIs by randomly
sampling from all 93 winters during 1920–2013 after
linearly regressing out the Niño-3.4 SST index (Fig. 3h)or by computing CIs from the 61 ENSO-neutral years(not shown). The CIs based on the ENSO residual
sample (Fig. 3h) are very similar to those on the ENSO
sample (Fig. 3e), indicating that internal atmosphericvariability likely underlies the uncertainty in the observed ENSO composite in analogy with the modelbased results. We note that the differences between theCIs from the atmospheric control simulation (Fig. 3g)
and the ENSO-residual observations (Fig. 3h) are evensmaller than those in Fig. 3f (not shown).

2) PRECIPITATIONThe ensemble-mean PR composite in CESM1 PACE
shows statistically significant drying in British Columbia,
the U.S. Pacific Northwest, and the Ohio Valley, andstatistically significant wetting along the southern coastof Alaska and throughout the southern United States,with maximum amplitudes in California and theSoutheast (Fig. 4a). This pattern resembles the observed
composite (Fig. 4b), except for the lack of pronounced
drying in the Ohio Valley–Upper South; however, as
noted earlier, this region is subject to large member-tomember variation (recall Fig. 2). The larger area ofstatistically significant PR anomalies in the modelcomposite compared to observations is likely a result of
averaging over 10 times as many ENSO events. Differencing the observed composite from the ensemble-mean
composite reveals that the model significantly overestimates the amplitude of the PR response to ENSO in
Southern California, Nevada, western Utah, and coastal
British Columbia, and significantly underestimates it in
the Ohio Valley–Upper South and Florida (Fig. 3c).

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FIG. 4. As in Fig. 3, but for PR (mm day21).

The modeled (Fig. 4d) and observed (Fig. 4e) CIs on
the ENSO PR composites show similar patterns, withthe largest amplitudes along the Pacific coast (maximum
values ;2–3 mm day21) and the southeast United States(maximum values ;1–2 mm day21). However, the model’s
CIs are larger over most of the western United States
and the Ohio Valley, and smaller over portions of thesoutheast United States (Fig. 4f) compared to observations. In these regions of overestimation (underestimation), the minimum (maximum) CI of any of
the 10 CESM1 PACE simulations exceeds (is less than)the observed CI, indicating that the model’s variability
is likely different from that of the real world in theselocations (see Fig. S8 for the CIs of each individual
simulation).
Applying the observed CIs to the model’s ensemblemean PR composite accentuates the statistical significance of the model’s overestimated PR response to
ENSO in the western United States (California, Nevada,Utah, western Colorado, and northwestern Arizona), as
the ensemble-mean composite PR values are found to

be larger than those in any of the 2000 observed bootstrapped composite samples (area outlined in red inFig. 4i). Similarly, the model’s underestimated PR response in the Ohio Valley and southern portions of
Ontario and Quebec becomes statistically significant
because of the smaller observed CIs. This additional
region is thus an area where there is a true bias in themodel’s forced ENSO response, which had been obscured by the model’s overestimated CI.
Finally, internal atmospheric variability accounts foralmost all of the uncertainty on the model’s ENSO PR
response (cf. Figs. 4g and 4d). Likewise, non-ENSOrelated variability (which we posit stems mostly frominternal atmospheric variability) accounts for most of
the uncertainty on the observed ENSO PR composite(cf. Figs. 4h and 4e).

c. Range of ENSO composites using bootstrapped
observations
How well do we know the spatial pattern and amplitude of SAT and PR responses to ENSO in the real

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FIG. 5. (a)–(i) Ten randomly selected bootstrapped ENSO composites of DJF SAT (8C) based on observations. Values not significant at
the 10% confidence level based on a two-sided t test are stippled.

world? As discussed above, the number of ENSO events
available for compositing during 1920–2013 may be insufficient to accurately separate the ENSO-forced response from unrelated climate variability. We make useof the 2000 bootstrapped ENSO composites based on
observations to address this question. To begin, we shownine of these composites selected at random for SAT(Fig. 5) and PR (Fig. 6). In analogy with the 10 PACE
simulations shown in Figs. 1 and 2, these synthetic observational composites display a range of amplitudes
and patterns. This range arises from a combination of
the different sets of ENSO events in each observational
composite (recall the bootstrapping methodology outlined in section 2) and the different sample of climate
anomalies unrelated to ENSO in each composite (recall
that only the latter contributes to the spread within agiven model’s PACE simulations). Although the general patterns are similar across the nine synthetic composites, there is considerable variation in amplitude andlevel of statistical significance. For example, statisticallysignificant warming (4–68C) over western Canada andAlaska is found in one randomly selected composite(Fig. 5i) but not in another (,28C; Fig. 5g). Similarly,

significant wetting occurs over Northern California(.3 mm day21) in one composite (Fig. 6g) but not in
another (,0.5 mm day21; Fig. 6d), while pronounced
drying is widespread over the Ohio Valley–Upper Southand southern Quebec in one composite (Fig. 6f) but notin another (Fig. 6g).
Next, we perform a more systematic investigation of
the 2000 observational bootstrapped ENSO composites,
sorting them according to their area-weighted amplitudes in selected regions of interest. For SAT, theseregions are the Northwest (NW: 548–708N, 1758–988W)
and Southeast (SE: 258–378N, 1028–858W) portions ofNorth America where the actual observed composite
shows significant warming and cooling, respectively. For
PR, we select the Pacific Northwest (PNW: 428–588N,
1258–1128W), Gulf states (GULF: 258–348N, 1008–
778W), and California (CA: 328–428N, 1258–1198W),
regions within which the majority of grid boxes show
significant rainfall signals in the actual observed composite (drying for PNW and wetting for GULF and CA).
These regions are depicted in Fig. 7 (SAT) and Fig. 9(PR). For illustration purposes, we display the 10th- and90th-percentile composite samples based on each

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FIG. 6. As in Fig. 5, but for PR (mm day21).

regional index. Table S1 lists the particular EN and LN
events and the number of times they are sampled foreach of the 10th- and 90th-percentile composite samples
for each regional index. A minimum of 11 distinct EN
events and 7 distinct LN events comprise each composite sample, and no single event is sampled more than
4 times (Table S1).
Figures 7a and 7b (7c and 7d) show the observed SAT
bootstrapped ENSO composites that lie at the 10th
and 90th percentiles, respectively, based on the NW(SE) SAT index. As expected, the index regions show
clear differences in SAT anomaly amplitude between
the lower- and upper-percentile composites on which
they are based. For example, the warming acrosswestern Canada and Alaska ranges from ;18–38C in the
10th-percentile NW composite (Fig. 7a) comparedto ;38–68C in the 90th-percentile NW composite(Fig. 7b). Similarly, the cooling over the southeastern
United States reaches 238C in the 10th-percentile SEcomposite (Fig. 7c) compared to 228C in the 90thpercentile composite (Fig. 7d). In addition to these local
differences in SAT amplitude, there are differences inmagnitude, pattern, and statistical significance over the
rest of continent. For example, the 90th-percentile SEcomposite, which has low-amplitude cooling over the
SE United States, shows high-amplitude warming overwestern Canada and Alaska. This is in contrast to the90th-percentile NW composite, which shows similarmagnitude warming over western Canada and Alaska
but stronger and more widespread areas of significant
cooling over the southern United States compared tothe 90th-percentile SE composite. Similarly, the amplitude and spatial extent of the cooling over thesouthern United States is comparable between the 90thpercentile NW and 10th-percentile SE composites, yet
the warming over Canada and Alaska is much larger
and more widespread in the former compared to thelatter. These results illustrate that composite SAT amplitudes in one region may be decoupled from those inanother.
To the extent that our random sampling methodology
does not introduce additional diversity due to differences among ENSO events (addressed below), the rangeof ENSO SAT composites in Fig. 7 illustrates what nature might have produced given a different sequence ofinternal variability independent of ENSO. That is, evenwith a sample of 18 EN and 14 LN events, the amplitude
and to a lesser extent the pattern of the North American

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5001

FIG. 7. Tenth- and 90th-percentile bootstrapped ENSO composites of observed DJF SAT (8C) based on the (a),(b) NW and (c),(d) SE
SAT indices (index regions outlined in black). Values not significant at the 10% confidence level based on a two-sided t test are stippled.
Panel insets show the accompanying SST (8C) composites in the tropical Pacific.

SAT response to ENSO are subject to considerable
uncertainty.
To what extent does the different composition of
ENSO events in each of the observed bootstrappedcomposites shown in Fig. 7 contribute to their different
SAT anomalies? As a first step in addressing this question, we show maps of the composite SST anomalies
in the tropical Pacific that accompany each SAT composite (insets in Fig. 7). All four SST composites show
similar patterns and amplitudes, with positive anomalies
along the equator (maximum values ;38–48C in thecentral basin 1658–1058W) flanked by weaker-amplitude(,1 C) negative values to the Northwest and Southwest.
If anything, the 90th-percentile composite based on the
NW SAT index, which features higher-amplitude warming across Canada and Alaska, shows weaker SST
anomalies in the central equatorial Pacific compared to its

10th-percentile counterpart (cf. Figs. 7a and 7b). However, this difference is likely a result of random chance,since there is no systematic relationship between the
Niño-3.4 SST and NW SAT indices across the 2000 observed composite bootstrapped samples as shown by thescatterplot in Fig. 8a. The large scatter indicates that the
precise pair of values of any particular composite, andby extension the pair of spatial patterns shown in Figs. 7aand 7b, is likely due to chance.
For example, for a given value of the NW SAT index
such as 3.18C, which is close to the value of the 90thpercentile sample (3.28C), there is a wide range ofpossible composite Niño-3.4 SSTA values (2.28 to
3.08C) across the 2000 bootstrapped ENSO composites(Fig. 8a). Thus, the small difference in Niño-3.4 SST
values (0.28C) between the 10th- and 90th-percentile
NW SAT index composites is unlikely to be the cause of

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FIG. 8. Scatterplots of ENSO composite values of the Niño-3.4 SST index against the (a) NW SAT index, (b) SE SAT index, (c) PNW PR
index, (d) GULF PR index, and (e) CA PR index based on 2000 observed bootstrapped samples. Blue and red symbols show the 10th- and90th-percentile values, respectively, of each index. SAT and SST values are in 8C and PR values are in mm day21.

the approximately twofold difference in their NW SAT
values (1.688 vs 3.28C). Conversely, for a given value of
the Niño-3.4 SST index, say 2.68C, the NW SAT index
can range from 18 to 48C. Similar remarks apply to the10th- and 90th-percentile composites based on the SE
SAT index (Fig. 8b). Finally, although there is a weak
linear dependence of the NW SAT index on the Niño3.4 SST index across the 2000 bootstrapped ENSO
composites (correlation coefficient 5 20.20), and to alesser extent of the SE SAT index on Niño-3.4 (correlation coefficient 5 0.01), removing this dependency via
linear regression analysis has virtually no effect on the
results (not shown), underscoring that differencesamong the 2000 individual bootstrapped composites areunlikely to be the result of sampling slightly different
sets of ENSO events.
To extend this analysis to all of North America, we
calculate the contribution to the observed CI that arises
from the linear dependence of the SAT composite
values at each grid box upon the Niño-3.4 composite
values across the 2000 bootstrapped samples. To obtain
this ‘‘ENSO contribution,’’ we first compute the CIs
using the 2000 SAT values of the bootstrapped composites

from which the composite Niño-3.4 SST index has been
linearly removed via regression analysis, and thensubtract it from the original CIs. This ENSO contribution is ,0.58C at all grid boxes, corresponding
to ,5% of the total CI, except near the Great Lakes
and a few isolated locations where it can reach up to
5%–15% (Fig. S9).
Repeating these analyses for PR, Fig. 9 shows theobserved bootstrapped ENSO composites that lie at the10th and 90th percentiles based on the PNW, GULF,
and CA PR indices defined above. All six composites
show similar spatial patterns, consisting of PR increases
over the Gulf states and along the Pacific coast of Canada and Alaska, and PR decreases over the PacificNorthwest and Ohio Valley–Upper South, similar to theensemble-mean composite (recall Fig. 4a). However,their magnitudes and areas of statistical significance varyconsiderably. For example, drying over the U.S. Northwest and Upper South is greater in amplitude and area ofsignificance in the 10th-percentile PNW composite compared to its 90th-percentile counterpart (Figs. 9a and 9b,
respectively). Similarly, PR increases over the southern
United States are larger and extend farther north in the1 JULY 2018

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FIG. 9. As in Fig. 7, but for observed PR (mm day21). Tenth- and 90th-percentile composites based on the (a),(b) PNW, (c),(d) GULF, and(e),(f) CA PR indices (index regions outlined in black).

90th-percentile GULF composite compared to the 10thpercentile composite (Figs. 9d and 9c, respectively). Finally, the only case with significant wetting over all ofCalifornia is the one based on the 90th percentile of the
CA PR index (Fig. 9f). The 10th–90th percentile PR
range within each index region is 20.60 to 20.24 mm day21

for PNW, 0.62 to 1.13 mm day21 for GULF, and 0.03 to
1.43 mm day21 for CA.
Similar tropical SSTA patterns accompany each PR
composite, with small variations in amplitude (panel
insets in Fig. 9). The 10th-percentile PNW composite(which has larger drying over the Pacific Northwest)

JOURNAL OF CLIMATEVOLUME 31% of samples(a) Northwest (NW)(b) Southeast (SE)

OC

20
10

GFD

L

20
10

M

20
10

TOG

A

20
10

T

20
10

20
10

MIR

20
10
20
10

CES

20
10
20
10

BES

-1

0

1

2
3
4
SAT anomaly (K)

5

6

-5

-4

-3

-2
-1
0
SAT anomaly (K)1% of samples

5004

2

FIG. 10. Histograms of ENSO composite values of the (a) NW and (b) SE SAT indices (8C). Gray bars denote values from the 2000
bootstrapped samples and red bars indicate actual values. From top to bottom: PACE simulations from MIROC (5 members), GFDL
CM2.1 (10 members) and CESM1 (10 members), and CAM5 TOGA simulations (10 members). Horizontal blue bar above each histogramindicates the 5%–95% CI range based on 2000 bootstrapped samples.

shows slightly weaker SSTA in the central equatorial
Pacific (1608–1308W), compared to its 90th-percentile
counterpart (Figs. 9a and 9b, respectively). However,this is likely a result of random chance since the PNW
index shows no systematic dependence on the Niño-3.4
SST index across the 2000 bootstrapped composite
samples (correlation coefficient 5 0.01; Fig. 8c). The
GULF and CA PR indices show slightly stronger dependencies on Niño-3.4 SST (correlation coefficients of
0.51 and 0.30, respectively), confirming the visual impression from the scatterplots (Figs. 8d,e) and consistent
with the slightly larger SSTA in the central equatorial
Pacific in the 90th-percentile composite sample (Figs. 9dand 9f, respectively) compared to the 10th-percentile
sample (Figs. 9c and 9e, respectively). However, themagnitude of the ENSO contribution to the CIs on theobserved PR composite is ,10% over the Gulf states
and California, and ,5% everywhere else except
southern Florida, where it reaches 20%–25% (Fig. S9).
Collectively, these results demonstrate that while
there is some effect associated with sampling different
sets of EN and LN events in the observed bootstrappedcomposites, it does not make a large contribution to theuncertainty in the SAT and PR ENSO composites, withsome regional exceptions as noted above. Thus, the diversity of amplitudes, patterns, and degree of statisticalsignificance among the ENSO composites shown in
Figs. 5–9 is primarily due to internal variability rather
than slightly different samples of ENSO events. In thiscontext, it is worth recalling the diversity in SAT and PR
composites across the individual members of the PACE
ensembles for which the set of ENSO events is identical.

d. Comparison across models
We summarize the amplitudes of the regional SAT
and PR indices across all 2000 bootstrapped ENSO
composites from observations and models in the histograms shown in Figs. 10 and 11. The actual composite
values are shown as red bars: one for observations; 10 foreach member of the CESM1 PACE, CESM1 TOGA, andCM2.1 PACE ensembles; and five for each member ofthe MIROC5 PACE ensemble. While the observed value
must lie in the middle of its bootstrapped samples byconstruction, this need not be the case for the models since
their bootstrapped samples were constructed by drawing
from among all ensemble members (although the average
across all members will lie at the peak of the distributionof the bootstrapped samples for a given model). The
horizontal blue bar above each dataset indicates the 5%–95%
CI range based on the bootstrapped samples.
As expected based on the results already presented, theCESM1 PACE ensemble shows a realistic mean value of
the NW SAT index but slightly overestimates the width ofits distribution (Fig. 10a). Also consistent with Fig. 1, twoof the CESM1 PACE ensemble members are obvious
outliers, falling in the lowest 1% of the distribution (bychance). The width and mean value of the NW SAT histograms based on the CM2.1 and MIROC5 PACE

1 JULY 2018

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% of samples(a) Pacific Northwest (PNW)

12
6

MIROC

12
6

GFDL

12
6

CESM

12
6

TOGA

12
6

OBS

-1.5

-1.0

-0.5
0.0
Precip (mm/day)

0.5% of samples(b) Gulf States (GULF)

12
6

MIROC

12
6

GFDL

12
6

CESM

12
6

TOGA

12
6

OBS

0.0

0.5

1.0
1.5
Precip (mm/day)

2.0% of samples(c) California (CA)

12
6

MIROC

12
6

GFDL

12
6

CESM

12
6

TOGA

12
6

OBS

-2

5005

ensembles are similar to those from the CESM1 PACE
ensemble, but the individual members are more evenly
distributed across the range of bootstrapped samples thanthose in CESM1 (Fig. 10a). The NW SAT histogram basedon the CESM1 TOGA ensemble is shifted to the right of
those based on observations and PACE simulations,overestimating the observed value (2.48C) by more than
50% in the ensemble mean (3.78C). For the SE SAT index,
the models generally show realistic distributions and CIs,
except for MIROC, which simulates an ensemble-meanvalue close to zero that is significantly different fromthe observed value of 21.28C (Fig. 10b). In all cases, thewidths of the distributions are considerably smaller for the
SE SAT index compared to the NW SAT index.
All model ensembles show realistic distributions for
PR in the PNW region, although MIROC5 is on thedrier side (Fig. 11a). There is more variation acrossmodels for PR in the GULF and CA regions, with all
members of MIROC5 substantially overestimating theobserved wetting, although there is overlap between thesimulated and observed bootstrapped CIs (Figs. 11b,c).
The most realistic distributions for GULF are thosefrom CESM1 PACE and TOGA (Fig. 11b), while those
for CA are CESM1 TOGA and CM2.1 (Fig. 11c). LikeCESM1 PACE and TOGA, CM2.1 shows one memberthat falls at the very dry end of the distribution for
GULF (Fig. 11b). The models generally simulate realistic CIs for all three PR indices, with the possible
exception of CESM1 PACE for CA, which is considerably larger than observed, although there is somemember-to-member variation (Fig. S8).
These portrayals of the bootstrapped ENSO composites for selected regional climate indices highlight theneed for large model ensembles, since a single simulation can alter the mean value, and to a lesser extent thewidth, of the distribution just by chance, confounding
model evaluation and model intercomparison.

e. El Niño versus La Niña composites

-1

0
1
2
Precip (mm/day)

3

4

FIG. 11. As in Fig. 10, but for the (a) PNW, (b) GULF, and(c) CA PR indices (mm day21). Note the wider x-axis range in(c) compared to (a) and (b).

Up to now, we have focused on the linear component of ENSO. Here we examine whether there are any
appreciable nonlinearities aside from polarity by comparing observed composites of the 18 EN events andthe 14 LN events separately. For ease of comparison
with EN, we show the LN composite with inverted sign(denoted 2LN). While the SAT composites show regional differences in amplitude associated with asouthward displacement of the continental-scale dipole
pattern in EN (Fig. 12a) compared to 2LN (Fig. 12b),these differences are not significant except near theGreat Lakes (Fig. 12c). Similarly, the PR composites
show local differences in magnitude associated withsouthward-shifted dipoles over the eastern United

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FIG. 12. Observed DJF SAT (8C) and PR (mm day21) composites for (a),(d) 18 El Niño events, (b),(e) 14 La Niña events (with reversedsign), and (c),(f) their difference (El Niño 1 La Niña). Values not significant at the 10% confidence level based on a two-sided t test arestippled.

States and along the Gulf of Alaska in EN (Fig. 12d)compared to 2LN (Fig. 12e), but none are significant
except those along the southern coast of mainland
Alaska (Fig. 12f). Our EN and LN composites are consistent with the analogous one-sided regression maps in
Hoerling et al. (2001); however, that study did not assess
differences between their one-sided regressions.

f. Flavors of El Niño
To what extent might different ‘‘flavors’’ of El Niño
affect the range of SAT and PR anomalies across the2000 observed bootstrapped composites? In particular,
if we sample only east Pacific (EP) or central Pacific(CP) El Niño events [defined according to the consensus
method of Yu et al. (2012); see also Graf and Zanchettin(2012) and Yu et al. (2015)] in our ENSO composites, do
we obtain significantly different anomalies and CIs? To
address this issue, we construct two additional 2000member sets of bootstrapped composites, which differ
from the original set by restricting the random samplingof all 18 El Niño events to those that fall in the EP
category (7) and to those that fall in the CP category(11); nothing is changed for the sampling of La Niña
events. Note that we maintain a total of 18 El Niño (and14 La Niña) events in these new CP and EP sets ofbootstrapped composites for consistency with the original ‘‘all El Niño’’ bootstrapped composites.
The CI maps based on the 2000 CP and 2000 EP
bootstrapped composites are very similar in pattern andamplitude for both SAT (Figs. 13a,b) and PR (Figs. 13c,d).
The slightly larger SAT CI values over Canada in CP
compared to EP are well within the range of what could be
expected by chance, at least according to the individual
members of CESM1 PACE, whose CIs are based on thesame set of ENSO events (Fig. S7). Similar results arefound by sampling only east Pacific nonconvecting (EPN)or east Pacific convecting (EPC) El Niño events (Johnson
and Kosaka 2016) in our 2000 observed bootstrapped
ENSO composites (not shown). Taken together, the results shown above reinforce the notion that ENSO diversity, whether in the form of differences in magnitude,
nonlinearities between EN and LN, or different ‘‘flavors’’of El Niño, does not have an appreciable effect on ourquantification of uncertainty on the observed ENSO SAT
and PR composites.
A separate but related question is whether the actual
observed CP and EP composites show significantly different SAT and PR anomalies. Both composites display
the familiar SAT dipole pattern across North America,
but EP exhibits larger statistically significant warming1 JULY 2018

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FIG. 13. CIs of 5%–95% on observed ENSO composites of DJF (a),(b) SAT (8C) and (c),(d) PR (mm day21) using only (a),(c) CP and(b),(d) EP El Niño events during 1920–2013. See text for details.

over Canada and Alaska compared to CP (maximum
values 48–58C vs 28–38C, respectively; Figs. 14a,b). In addition, the region of significant cooling is confined to thesouthern tier of U.S. states in CP but penetrates into the
Mid-Atlantic states in EP. The area extending southeastward from northern Saskatchewan to the U.S. central
Atlantic coast shows statistically significant differences
between EP and CP, but the rest of Canada does not,despite the nearly twofold difference in composite SAT
amplitudes (Fig. 14c). PR shows a very similar pattern
between the two sets of composites, with somewhat larger
amplitudes for EP compared to CP (Figs. 14e and 14d,
respectively), but these differences are not statisticallysignificant except at a few locations (Fig. 14f). SAT and
PR differences between EPN and EPC composites arealso generally not statistically significant over North
America, as shown by Johnson and Kosaka (2016).

2015; Jong et al. 2016). Repeating our observational
ENSO composites for late winter (shown in Fig. S10), we
confirm that in addition to Southern California, positive
PR anomalies occur with larger amplitudes (statisticallysignificant values of 0.2–0.5 mm day21) in FMA comparedto DJF over the U.S. Southwest desert and portions of
Kansas, Nebraska, and South Dakota (cf. Fig. S10b with
Fig. 4b). The increased PR in FMA is accompaniedby stronger cooling (statistically significant amplitudesof 18–28C) over Arizona, New Mexico, and parts ofColorado and Kansas (Fig. S10a) compared to DJF(Fig. 3b). Elsewhere, ENSO composite values are considerably weaker in late winter than in midwinter, bothfor PR and SAT. For example, warming over Canada andAlaska is only half as strong in FMA compared to DJF,
and drying over the interior Pacific Northwest is weak andinsignificant in late winter.

g. Late-winter ENSO composites
While the primary focus of this study is on DJF, we
briefly report on FMA, as rainfall over Southern California
shows a larger ENSO signal in this season (L’Heureux et al.

4. Discussion and summary
This study has evaluated the role of sampling variability in ENSO composites of winter SAT and PR over

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FIG. 14. Observed ENSO composites of DJF SAT (8C) and PR (mm day21) using only (a),(d) CP and (b),(d) EP El Niño events during
1920–2013, and (c),(f) their difference (CP–EP). Values not significant at the 10% confidence level based on a two-sided t test are stippled.

North America during the period 1920–2013 in observations and ensembles of ‘‘Tropical Pacific Pacemaker’’coupled model simulations with CESM1, CM2.1, and
MIROC5. The individual members of each model ensemble show a surprising amount of diversity in their
ENSO composites, despite the fact that they are constructed from the same observed set of 18 EN and 14 LN
events. For a given model, this ensemble spread can only
be due to sampling variability, that is, aliasing of internalvariability that is unrelated to ENSO. In the case of
CESM1, for which a lengthy atmosphere-only control
simulation is available, we showed that this sampling
variability arises from internal atmospheric dynamics
rather than coupled ocean–atmosphere processes. Similar ENSO composite spread is evident in an uncoupled(atmosphere-only) model ensemble with observed timevarying tropical SSTs prescribed at the lower boundary(the CESM1 TOGA ensemble).

1 JULY 2018

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Are the observed ENSO composites subject to asimilar level of uncertainty as those in the Pacemakerensembles? What might the observed ENSO composite
have looked like under a different permutation of natural variability unrelated to ENSO? To address thesequestions, we constructed 2000 synthetic ENSO composites from the observations using random samplingtechniques. These synthetic composites provide information on the range of spatial patterns and amplitudes associated with imperfect estimation of the forced
ENSO signal. The observed SAT composite shows astatistically significant dipole pattern of positive anomalies over western Canada and Alaska and negative
anomalies over the southeastern United States. But although all 2000 synthetic ENSO composites show positive SAT values in the NW and negative SAT values in
the SE, their amplitudes vary by approximately a factorof 2.5 between the 5th- and 95th-percentile composite
samples (1.38 to 3.48C for the NW and 20.78 to 21.78C
for the SE). The observed PR composite shows significant wetting over the Gulf states and parts of California,
Arizona, and New Mexico, and significant drying over
the Ohio Valley–Upper South and parts of the interior
Pacific Northwest. However, the 5%–95% uncertainty
range on the magnitudes of these regional composite PR
anomalies is substantial: 0.55 mm day21 to 0.89 mm day21in the GULF; 20.15 mm day21 to 20.69 mm day21 in
the PNW (and also the Ohio Valley–Upper South);and 20.07 mm day21 to 11.18 mm day21 in CA. Whileprevious studies highlight that the strong EN events of
1957/58, 1982/83, and 1997/98 each brought copious
amounts of rainfall to CA (Siler et al. 2017; Lee et al.
2018), our results are not unduly influenced by the numberof times these events are sampled in our synthetic composites. In particular, these three events account for,16% of the ENSO events sampled in 89% of the synthetic ENSO composites, consistent with the results
shown in Figs. 8, 9, and S9, and make up 9% and 12.5% of
the events sampled in the 10th- and 90th-percentile PRcomposites based on CA PR, respectively (Figs. 9e,f).
Although the synthetic ENSO composites based on
observations are necessarily constructed from different
combinations of EN and LN events, differences in magnitude of the composite Niño-3.4 SST index make only aminor (,5%) contribution to their spread over most of
North America, with slightly higher values (up to 10%)for SAT near the Great Lakes and PR over California andportions of the SE United States, and up to 25% for PR
over central Florida. Removing this dependence on thecomposite Niño-3.4 values results in a slight (i.e., on theorder of a few grid boxes) expansion of the regions covered by robust ENSO signals, but does not quantitatively
affect the results (Fig. S11). Other forms of ENSO

5009

diversity, such as nonlinearities between EN and LN or
different ‘‘flavors’’ of El Niño (EP vs CP), also do notappreciably affect our quantification of uncertainty on theobserved ENSO SAT and PR composites.
Our results have implications for ENSO reconstructions based on paleoclimate proxy records of SAT and
PR over North America. In particular, such ENSO reconstructions will also be subject to uncertainties associated with sampling variability, even if the proxies are
perfect indicators of winter climate anomalies. Judicious
choices of proxy record locations based on the uncertainties provided here may help to narrow this range;
another ameliorating factor may be if the proxy records
integrate climate signals over a broader seasonal window, as this may help to reduce aliasing from naturalvariability unrelated to ENSO (although it may also
weaken the ENSO signal).
Our results have broad implications for how to evaluate the realism of ENSO signals in models. In particular, uncertainty in the pattern and amplitude of theobserved ENSO composite necessitates an approach tomodel assessment that considers not only the model’s
forced response to ENSO, but also its representation ofinternal variability unrelated to ENSO. In the Pacemaker ensembles, we can determine the forced responseby averaging ENSO composites across all members of agiven model. Using the 2000 synthetic ENSO composites constructed for each model simulation and for observations, we can discriminate between true model
biases in the forced ENSO response and apparent model
biases that arise from limited sampling of internal variability unrelated to ENSO.
Applying this approach to the CESM1 Pacemakerensemble, we find that the model significantly overestimates internal variability (and hence ENSO composite spread) of SAT over Alaska and parts of theeastern and southwestern United States, and also significantly overestimates (underestimates) internal variability of PR over the western (southeastern) UnitedStates. Taking these differences in internal variability
into account, we are able to reveal true biases in themodel’s forced ENSO response, including a significant
underestimation of warming over the central Canadianprovinces and U.S. Upper Midwest, a significant underestimation of wetting (drying) over Florida (OhioValley–Upper South), and a significant overestimationof wetting (drying) over California and Nevada (coastal
British Columbia). Somewhat different model biases in
the forced ENSO response are apparent in the uncoupled CESM1 TOGA ensemble for reasons discussed
in the appendix. Observational uncertainty in tropical
SSTs used as boundary forcing for the models represents
an additional potential source of discrepancy between

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FIG. A1. CIs of 5%–95% on ENSO composites of DJF (a) SAT (8C) and (b) PR (mm day21) from the CESM1 TOGA ensemble based on
2000 bootstrapped samples.

the observed and simulated ENSO composites, andmerits investigation.
In summary, even with nearly a century of observations, quantification of the canonical influence of ENSO
on North American climate is subject to considerable
uncertainty due to aliasing of unrelated climate variability. This observational uncertainty must be properly
accounted for when evaluating ENSO responses inclimate models. In particular, discriminating between
true model biases in the forced response to ENSO, andapparent model biases that arise from limited samplingof internal variability unrelated to ENSO, is essential.
Acknowledgments. We thank Dr. Tingting Fan for
conducting the CESM1 tropical Pacific PACE simulations, Dr. Yu Kosaka for providing the CM2.1 and

FIG. A2. ENSO composites of DJF SAT (8C) and SLP (hPa; contours) for the (a) CESM1 TOGA ensemble mean, (b) CESM1 PACE
ensemble mean, (c) observations, and differences between (d) TOGA ensemble mean and observations, (e) PACE ensemble mean andobservations, and (f) TOGA and PACE ensemble means. Contour interval is 2 hPa, with negative values dashed and the zero contour
thickened. Stippling in (a),(b),(c),(f) indicates SAT values not significant at the 10% confidence level based on a two-sided t test. Stipplingin (d),(e) indicates that the observed value lies outside of the 5%–95% values from the model’s 2000 bootstrapped composites, and red
contours indicate the observed value lies outside any of the model’s bootstrapped composites.

1 JULY 2018

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5011

FIG. A3. As in Fig. A2, but for PR (mm day21).

MIROC5 PACE simulations, and Dr. Jin-Yi Yu for the
EP and CP classification of El Niño years. We appreciate
the helpful comments and suggestions from the twoanonymous reviewers and Dr. Michael Alexander.
Graphics and data processing were performed with the
NCAR Command Language (http://dx.doi.org/10.5065/
D6WD3XH5). The National Center for AtmosphericResearch (NCAR) is sponsored by the National Science
Foundation. K. A. M. was supported by an Advanced
Study Program Postdoctoral Fellowship at NCAR, and
A. S. P was supported in part by a grant from the NOAA
MAPP Program.

APPENDIXCESM1 Pacemaker versus TOGA Simulations
Are there any systematic differences in ENSO composites from the coupled (PACE) and uncoupled(TOGA) simulations with CESM1, and if so, why? The
spread across the 2000 bootstrapped ENSO composites isvery similar between TOGA and PACE, for both SAT(cf. Fig. A1a and Fig. 3d) and PR (cf. Fig. A1b and Fig. 4d),
consistent with the dominant role of internal atmosphericvariability on ENSO composite uncertainty discussed in
section 3b. However, the ensemble-mean ENSO composite (i.e., the forced response to ENSO) differs somewhat between TOGA and PACE. In particular, theensemble-mean SAT composite shows oppositely signed
biases, with TOGA significantly overestimating theobserved warming over northern Canada by up to 38C,
and PACE significantly underestimating it over southcentral Canada by about the same amount (Fig. A2). Thus,
neither model configuration is clearly superior in terms of
SAT amplitude, although the spatial pattern is more realistic in TOGA than PACE [the pattern correlation (r) of
the ensemble-mean SAT composite against the observed
SAT composite is 0.91 in TOGA compared to 0.75 in
PACE, and the lowest r of any of the individual TOGA
ensemble members (0.82) exceeds the highest r from anyof the individual PACE ensemble members (0.81)]. The
TOGA ensemble-mean PR composite shows realistic
magnitudes of wetting over Southern California and
Nevada and drying over coastal British Columbia,
areas where PACE was biased high; however, TOGA
overestimates the drying over parts of Oregon, Washington, and Montana and underestimates it in interior
British Columbia, areas where PACE was not significantly biased (Fig. A3). While the spatial pattern of the
TOGA ensemble-mean PR composite bears a closer
resemblance to the observed PR composite than does
PACE (r 5 0.76 for TOGA and 0.63 for PACE), thereis overlap between the lowest pattern correlation in
TOGA (0.64) and the highest in PACE (0.68) acrossthe individual ensemble members.
In summary, there are systematic differences in the
ENSO-forced SAT and PR responses between the
TOGA and PACE configurations of CESM1, with
TOGA showing an improved representation of thespatial pattern but not amplitude of SAT, and of the PR

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FIG. A4. ENSO composites of DJF PR (mm day21) and SLP (hPa; contours) for the (a) CESM1 TOGA ensemble mean, (c) CESM1PACE ensemble mean, (e) observations (20CR), and differences between (b) TOGA ensemble mean and observations, (d) PACE ensemble mean and observations, and (f) TOGA and PACE ensemble means. Contour interval is 2 hPa, with negative values dashed and thezero contour thickened. Stippling in (a),(c),(e),(f) indicates SLP values not significant at the 10% confidence level based on a two-sided
t test. Stippling in (b),(d) indicates that the observed SLP value lies outside of the 5%–95% values from the model’s 2000 bootstrappedcomposites.

magnitudes over Southern California and Nevada
and coastal British Columbia, compared to PACE. A
10-member CESM1 ensemble with specified observed
time-evolving SSTs (and sea ice) over the entire globe
yields virtually identical results to TOGA (not shown);thus, the differences between PACE and TOGA are
unlikely to result from ENSO-related SST anomalies inthe extratropics.

What is the origin of the systematic differences in
ENSO responses between TOGA and PACE? To address this question, it is helpful to view the surface
climate impacts of ENSO within the context of thelarge-scale atmospheric circulation that drives them.
The ensemble-mean ENSO composites of SLP fromTOGA and PACE show negative anomalies over the
North Pacific, with maximum values of 8–10 hPa near

1 JULY 2018

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DESER ET AL.

FIG. A5. ENSO composites of DJF PR (mm day21) for (a) 20CR, (b) GPCP, and(c) ERA20C. Stippling indicates values not significant at the 10% confidence level based ona two-sided t test. Composites based on 1920–2013 for (a),(c), and 1979–2013 for (b).

the Aleutian Islands, similar to observations (Figs. A2and A3; see also D17). However, the orientation of theisobars is more zonal in PACE compared to the NW–SE
tilt evident in observations and TOGA. The SLP difference between TOGA and PACE indicates onshore
flow of mild maritime air into western Canada, which
may account for the greater warming (Fig. A2f) andwetting (Fig. A3f) in this region in TOGA relative to
PACE. Farther south, the offshore flow component in
TOGA compared to PACE is likely responsible for the
reduced wetting over California and neighboring states(Fig. A3f).
The circulation differences in TOGA and PACE, inturn, may be linked to differences in their tropical PR
responses via Rossby wave dynamics as shown in Fig. A4.
The tropical PR response in TOGA shows wetting overthe central Pacific and drying over the far western Pacific,
similar to observations but with reduced amplitude(Figs. A4a,e). Here, the observed tropical PR composite
is based on 20CR, but similar results are found using
GPCP (limited to the satellite period starting in 1979) and
ERA20C (Fig. A5). In PACE, this entire pattern is shifted to the west and more equatorially confined, withmaximum wetting over the western equatorial Pacific andmaximum off-equatorial drying over the eastern Indian
Ocean (Fig. A4c). This westward displacement likely
reflects the influence of mean-state biases in the fully
coupled CESM1, in particular a westward-extended equatorial SST ‘‘cold tongue’’ that anchors a narrow ‘‘double
ITCZ’’ on either side of the equator (not shown; similar

mean-state biases are found in CM2.1 and MIROC5).
Thus, the PACE protocol is not a panacea because of theinfluence of mean-state biases on CESM1’s response toobserved SSTA. The difference in tropical PR responses
between TOGA and PACE shows negative values in
the western Pacific of up to 210 mm day21 and weakeramplitude positive values in the central Pacific (Fig. A4f).
This is accompanied by an arching SLP wave train over the
North Pacific that appears to emanate from the negative
precipitation center in the western tropical Pacific, possibly
indicative of a ‘‘short wavelength’’ Rossby wave response.
This circulation response, in turn, drives the surface climate response differences noted above.
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