The role of the North Atlantic Oscillation in
European climate projections

Clara Deser, James W. Hurrell & Adam
S. Phillips

Climate Dynamics
Observational, Theoretical andComputational Research on the Climate
SystemISSN 0930-7575
Volume 49
Combined 9-10
Clim Dyn (2017) 49:3141-3157
DOI 10.1007/s00382-016-3502-z

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Clim Dyn (2017) 49:3141–3157
DOI 10.1007/s00382-016-3502-z

The role of the North Atlantic Oscillation in European climate
projections
Clara Deser1· James W. Hurrell1 · Adam S. Phillips1

Received: 14 July 2016 / Accepted: 16 December 2016 / Published online: 30 December 2016
© The Author(s) 2016. This article is published with open access at Springerlink.com

Abstract This study highlights the expected range of projected winter air temperature and precipitation trends overthe next 30–50 years due to unpredictable fluctuations of
the North Atlantic Oscillation (NAO) superimposed upon
forced anthropogenic climate change. The findings are
based on a 40-member initial-condition ensemble of simulations covering the period 1920–2100 conducted with theCommunity Earth System Model version 1 (CESM1) at 1°
spatial resolution. The magnitude (and in some regions,even the sign) of the projected temperature and precipitation trends over Europe, Russia and parts of the Middle
East vary considerably across the ensemble dependingon the evolution of the NAO in each individual member.
Thus, internal variability of the NAO imparts substantial
uncertainty to future changes in regional climate over the
coming decades. To validate the model results, we apply
a simple scaling approach that relates the margin-of-error
on a trend to the statistics of the interannual variability. In
this way, we can obtain the expected range of projected climate trends using the interannual statistics of the observed
NAO record in combination with the model’s radiativelyforced response (given by the ensemble-mean of the 40
simulations). The results of this observationally-based
estimate are similar to those obtained directly from the
CESM ensemble, attesting to the fidelity of the model’s
Electronic supplementary material The online version of thisarticle (doi:10.1007/s00382-016-3502-z) contains supplementarymaterial, which is available to authorized users.
* Clara Desercdeser@ucar.edu
1

Climate and Global Dynamics Division, National Center
for Atmospheric Research, P.O. Box 3000, Boulder,
CO 80307, USA

representation of the NAO and the utility of this approach.
Finally, we note that the interannual statistics of the NAO
and associated surface climate impacts are subject to uncertainty due to sampling fluctuations, even when based on a
century of data.
Keywords North Atlantic Oscillation · Climate
projections · Climate prediction · Climate variability

1 Introduction
The North Atlantic Oscillation (NAO), the dominant modeof atmospheric circulation variability over the North Atlantic/European sector, is a leading governor of wintertime climate fluctuations in Europe, the Mediterranean, parts of the
Middle East and eastern North America over a wide rangeof time scales from intra-seasonal to multi-decadal (e.g.,
Hurrell 1995; Hurrell et al. 2003). The NAO’s prominent
upward trend from the 1950s to the 1990s caused large
regional changes in air temperature, precipitation, windand storminess, with accompanying impacts on marine
and terrestrial ecosystems, and contributed to the accelerated rise in global mean surface temperature (e.g., Hurrell
1996; Ottersen et al. 2001; Thompson et al. 2000; Visbeck
et al. 2003; Stenseth et al. 2003). As an intrinsic mode ofvariability of the large-scale atmospheric circulation, theNAO requires no external forcing for excitation (e.g., Hurrell and Deser 2009; Branstator and Selten 2009). Indeed,
its time series is well characterized as a predominantly stochastic process with an e-folding time scale of 10–14 days
(Feldstein 2000; Wunsch 1999; Deser et al. 2010). Thus,
interannual and longer time scale fluctuations of the NAO
need not have an external cause (Bracco et al. 2004; Hurrell
et al. 2004; Deser and Phillips 2009). On the other hand,

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changes in surface boundary conditions such as sea surface
temperature (SST) anomalies in the tropics (Hoerling et al.
2001, 2004; Bracco et al. 2004), winds in the stratosphere(Scaife et al. 2014; Kidston et al. 2015), and radiativechanges associated with increasing GHG concentrations(Selten et al. 2004; Deser et al. 2012) may also influence
the NAO. Although some predictive skill for the NAO may
be obtained through knowledge of anomalous boundary
conditions, it is limited to lead times of a year or two at best
(Scaife et al. 2014; Dunstone et al. 2016).
European climate over the coming decades and centuries will continue to be influenced by the NAO. However,with increases in GHG concentrations due to the burningof fossil fuels, human-induced climate change will also
play a role. The relative magnitudes of the climate impacts
induced by the naturally-occurring NAO and by anthropogenic factors will depend on the time horizon (e.g., next
few decades vs. end of the twenty-first century), time-scale(interannual vs. multi-decadal), and parameter (temperature vs. precipitation) of interest (e.g., Deser et al. 2012).
Anthropogenic forcing may also alter the characteristics of
the NAO itself. For example, some studies project a slight
positive shift in the probability distribution of NAO phaseand a small northeastward displacement of its centers-ofaction by the end of the twenty-first century (Ulbrich andChristoph 1999; Branstator and Selten 2009; Deser et al.
2012; Barnes and Polvani 2015).
The purpose of this study is to examine the impact of
the NAO on projected changes in winter (December-March
average) terrestrial surface air temperature (SAT) and precipitation (P) over the next 30–50 years. We make use of a
40-member ensemble of climate change simulations underhistorical and RCP8.5 radiative forcing scenarios for theperiod 1920–2100 conducted with the Community Earth
System Model Version 1 (CESM1; Hurrell et al. 2013).
Each member of the CESM “Large Ensemble” (hereafterreferred to as the CESM-LE) is subject to the same radiative forcing, but begins from a slightly different atmospheric state in 1920 (Kay et al. 2015). This infinitesimal
perturbation to the initial atmospheric state is enough tocause the individual ensemble members to diverge overtime. The resulting ensemble spread, due entirely to unpredictable internally-generated climate variability, yields
a range of future climate projections (Deser et al. 2014).
Here, we focus on the range of projected climate trends that
results from the superposition of the GHG-forced climatechange signal and intrinsic variability of the NAO.
To validate the model results, we compare the pattern
and magnitude of NAO variability in the historical portionof the CESM-LE to observations. We also make use of thefact that the expected range of trends due to sampling fluctuations is related to the magnitude of the interannual variability for a Gaussian time series (Thompson et al. 2015).

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C. Deser et al.

This relationship allows us to estimate the impact of theunforced component of NAO variability on future climate
trends purely from observations. This approach provides a
hybrid assessment of the combined influence of anthropogenic climate change [determined from the ensemble-meanof the CESM-LE or from the multi-model Coupled Model
Intercomparison Project phase 5 (CMIP5) archive (Taylor
et al. 2012)] and observed NAO variability on climate overthe coming decades. Finally, we assess the extent to which
NAO variability changes with climate change, using the
CESM-LE as our test bed.
The rest of the paper is structured as follows. Section 2
provides details of the CESM-LE simulations, observational data sets, and methodology. Results are given in
Sect. 3 and summarized in Sect. 4. The main paper contains results for trends over the next 30 years (2016–2045),
while the Supplemental Material shows results for trends
over the next 50 years (2016–2065).

2 Data and methods
2.1 The CESM1 large ensemble (CESM‑LE)
and accompanying control simulations
We make use of the new 40-member ensemble of climatechange simulations under historical and RCP8.5 radiative
forcing scenarios for the period 1920–2100 conducted with
CESM1. As mentioned above, each member begins fromthe identical state in 1920, except for a random perturbationon the order of round-off error ­(10−14 K) to the atmospherictemperatures. The initial state in 1920 is obtained from thefirst member, which begins in 1850 after branching from a
400-year control run (Kay et al. 2015).
We also make use of two lengthy control simulations
conducted with CESM1 under constant 1850 radiative conditions: a 2200-year control run using the fully-coupledconfiguration (hereafter termed the “coupled control run”),
and a 2600-year control run using only the atmosphericmodel component coupled to the land model component
from CESM1 with a specified repeating seasonal cycleof sea surface temperatures (SSTs) and sea ice conditions
taken from the long-term climatology of the fully-coupledcontrol run (hereafter termed the “atmospheric controlrun”).
2.2 The CMIP5 ensemble
We also make use of simulations in the CMIP5 archive. We
use all available models that conducted simulations for theperiod 2016–2065 under the RCP8.5 radiative forcing scenario. If more than one simulation exists for a given model,
we use only the first one. We then average all 38 model

The role of the North Atlantic Oscillation in European climate projections﻿	

simulations together to obtain the multi-model mean, which
we interpret as the forced response to the RCP8.5 scenario.

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grid box are regressed onto the standardized PC to produce the associated trend regression maps.

2.3 Observational datasets
We make use of three observational data sets that provide
monthly mean coverage over the Northern Hemisphere
continents for the period 1920–2012. We obtain surface
air temperature (SAT) from the GISS Surface Temperature
Analysis (GISTEMP) on a 2° latitude × 2° longitude gridand smoothed with a 250-km spatial filter (Hansen et al.
2010). Other SAT data sets such as the Climatic Research
Unit Temperature, version 4, (CRUTEM4; Osborn andJones 2014), and the Merged Land–Ocean Surface Temperature analysis (MLOST), version 3.5, (Vose et al. 2012)
give similar results (not shown). We obtain precipitation(P) from the Global Precipitation Climatology Centre(GPCC) data set on a 2.5° latitude × 2.5° longitude grid
(Becher et al. 2013). Finally, we obtain sea level pressure(SLP) from the Twentieth Century Reanalysis, version 2,(Compo et al. 2011) on a 2° latitude × 2° longitude grid.
2.4 Methods
For each observational dataset and the historical portion ofeach CESM-LE simulation, we compute monthly anomalies by subtracting the long-term (1920–2012) monthly
means from the corresponding month of each year. Similarly, for the 2016–2045 (2016–2065) segment of each
CESM-LE simulation, we compute monthly anomalies
by subtracting the long-term monthly means based on
2016–2045 (2016–2065) from the corresponding monthof each year. We then form 4-month winter (December–March; DJFM) averages from the monthly anomalies.
Finally, we compute linear trends over the next 30 years(2016–2045) and next 50 years (2016–2065) using linear
least-squares regression analysis.
We compute the leading empirical orthogonal function (EOF) of detrended DJFM SLP anomalies over thedomain [20–70°N and 90°W–40°E] following Hurrell
and Deser (2009). This is done for observations using thehistorical period 1920–2012, and for the CESM-LE using
the historical and future portions of the simulations. The
corresponding principal component (PC) time series isthen standardized, and all spatial patterns are produced
by regressing detrended anomalies of SLP, SAT, and P ateach grid box on the standardized PC. In the case of the
CESM-LE, we also compute the leading EOF of DJFM
SLP trends across the 40 ensemble members, similar to
the approach in Deser et al. (2014). The corresponding
PC record consists of 40 values, one for each ensemble
member. The 40 SLP, SAT and P trend values at each

3 Results
3.1 Contrasting future NAO trends
Figure 1 illustrates the range of projected changes in
winter (DJFM) SLP, SAT and P over the next 30 years(2016–2045) resulting from the superposition of naturalvariability and forced climate change as simulated by the
CESM-LE. For this figure, we have picked two ensemble
members, simulations 13 and 25, that show large-amplitude SLP trend patterns that resemble the negative andpositive phase of the NAO, respectively. Following convention, a positive NAO state is defined when SLP overthe northern (central) North Atlantic is below (above)normal, and vice versa (e.g., Hurrell 1995). The contrasting atmospheric circulation trends in the two simulations
give rise to different SAT and P trends over the adjacentcontinents. In particular, simulation 13 shows coolingover parts of northern Europe and pronounced warming over Greenland, while simulation 25 features strong
warming over northern Europe and cooling over Greenland. These regions of cooling and enhanced warming
result from different patterns of horizontal temperatureadvection implied by the contrasting SLP trends. These
different SAT trends occur despite the fact that both simulations were subject to the identical radiative forcing
and were conducted with the same model, highlighting
the role of internal atmospheric circulation variability inany single model run.
The difference in the P trends in the two simulations
is even more striking, with P increases (decreases) oversouthern Europe in simulation 13 (25), and oppositesigned changes over northern Europe. Such P trends are
consistent with the anomalous large-scale atmosphericcirculation changes. From these two illustrative examples, it is clear that internal variability in the large-scaleatmospheric circulation can have a large impact on themagnitude and sign of future climate trends over Europe.
The full set of SLP, SAT and P trend maps based on
2016–2045 for each of the 40 CESM-LE members isshown in Figs. S1 and S2.
Trend maps for the 50-year period 2016–2065 for twocontrasting members (#19 and #31) of the CESM-LE are
shown in Fig. S3. Even over the next 50 years, the polarityof the NAO and associated P trends over Europe is uncertain. And although the SAT trends in both simulations are
positive, their amplitudes can vary substantially dependingon the internal variability.

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(a)

(b)

(c)

(d)

Fig. 1  Future 30-year trends (2016–2045) in winter (a, b) SAT (°C
per 30 years; color shading) and (c, d) precipitation (mm d­ ay−1 per
30 years; color shading) from simulations 13 and 25 of the CESM1

Large Ensemble, chosen for their contrasting SLP trends (contours;
interval = 1 hPa per 30 years with negative values dashed)

3.2 Generalizing the range of trends due to the NAO

the forced SLP trend pattern projects onto the positive
phase of the NAO, its amplitude is negligible comparedto the internal variability of NAO trends. In particular,
the forced SLP trend magnitudes are <1 hPa per 30 years
everywhere except over Hudson’s Bay and the Barents
Sea where they reach 1–2 hPa per 30 years. [Hereafter,
we shall omit the units “per 30 years” when citing trend
values]. Similar forced SLP trends are obtained from theensemble-mean of the CMIP5 multi-model archive (seebelow). The forced component of SAT trends shows
warming everywhere, with magnitudes of 1–2 °C over
Europe, northern Africa, Greenland and the eastern
U.S., and slightly larger warming (2–3 °C) over Russia and Canada, with the largest SAT increases (3–4 °C)
surrounding Hudson’s Bay (Fig. 2b). The forced component of P trends is generally small (<0.15 mm day-1 inabsolute value), with decreases over northern Africa andsouthern Europe and increases elsewhere (Fig. 2f).

To generalize these results, we compute the leading EOF
of winter SLP trends (2016–2045) across the 40 ensemble
members of the CESM-LE as described in Sect. 2.4 (seealso Deser et al. 2014). The leading mode explains 45% of
the SLP trend variance and resembles the NAO, reinforcing the notion that the NAO is not only a dominant modeof variability on interannual time scales, but also on multidecadal time scales (Fig. 2a). We then regress the 40 SLP,SAT and P trend values at each grid box on the standardized SLP trend PC, and multiply these regression values
by two. The resulting regression maps thus represent thetrends associated with a two standard deviation departure(σ) of the SLP trend PC.
This leading pattern of internal variability of 30-year
trends can be compared to the forced component of
30-year trends, the latter obtained by averaging thetrends from all 40 ensemble members (Fig. 2b). While

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 2  Impact of the NAO on future 30-year climate trends (2016–2045). a Regressions of winter SLP and SAT trends upon the normalized leading PC of winter SLP trends in the CESM1 Large Ensemble,
multiplied by two to correspond to a two standard deviation anomalyof the PC; b CESM1 ensemble-mean winter SLP and SAT trends; cb − a; d b + a. SAT in color shading (°C per 30 years) and SLP in contours (interval = 1 hPa per 30 years with negative values dashed). e–h
as in a–d but for precipitation (mm d­ ay−1 per 30 years) in place of
SAT

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Adding and subtracting the leading pattern of internalvariability to/from the forced response yields the 5–95%
range of trend values due entirely to the superposition of
the NAO and human-induced climate change (see Fig. 2c,d for SLP and SAT, and Fig. 2g, f for SLP and P). These
NAO-induced “book-ends” of future climate trends arevery similar to those depicted in the individual simulations shown earlier (Fig. 1), but instead of case studies,
they are based on the dominant structure of internal atmospheric circulation variability across all 40 ensemble members superimposed upon the forced response. These future30-year SAT and P trends exhibit substantially different
patterns, polarities and magnitudes depending on the signof the NAO trend. In particular, warming over northern
Europe and Russia can vary from near-zero (or even slightcooling) to more than 4 °C, and over northern Africa andparts of the Middle East from <1 °C to >2 °C, dependingon whether the NAO trend is +2σ or −2σ (Fig. 2c, d). Even
more striking, the out-of-phase relationship between P
trends over northern and southern Europe associated with
the NAO (Fig. 2e) remains evident over the next 30 years
despite human-induced climate change. That is, the sign of
P trends in these locations is dictated by the polarity of the2σ NAO trend (Fig. 2g, h).
The 5–95% range of NAO-induced climate trends overthe next 50 years (2016–2065) is shown in Fig. S4. In analogy with the results based on 30-year trends, the leading
EOF of 50-year SLP trends resembles the NAO in spatial
structure, and its amplitude is much larger than the forced
component of SLP trends. As a result, SLP trends over the
next 50-years may display a positive or negative phase of
the NAO. These opposing circulation trends impact themagnitude of future warming, but do not change its sign
(Fig. S4 c,d). However, they do alter the sign of P trends
over southern Europe, and modulate the amplitude of
future P increases over northern Europe and eastern North
America (Fig. S4 g,h). These NAO-induced “book-ends”
of climate trends over the next 50 years are very similar to
those depicted in the two individual simulations shown in
Fig. S3.
3.3 Validating the model’s NAO
How well does CESM1 depict the NAO and associated surface climate impacts? Clearly, the observational record is
too short to validate the trend characteristics of the model’s
NAO, but at least its interannual aspects can be assessed.
Figure 3 compares the leading EOF of interannual winter
SLP variability based on detrended data during 1920–2012
from observations (20CR) and the CESM-LE (note that thehistorical portion of the CESM-LE is used to match observations). For the model, we have computed the EOF foreach ensemble member individually, and then averaged the13

C. Deser et al.

40 corresponding regression patterns based on the standardized PC time series to obtain robust ensemble-mean
statistics. However, nearly identical results are obtained byapplying EOF analysis to the concatenated set of detrended
SLP anomalies from the 40 simulations (not shown). The
observed interannual EOF accounts for 47% of the variance of detrended SLP anomalies during 1920–2012, anddepicts the NAO with one center-of-action located between
Greenland and Scandinavia, and the other of opposite
polarity centered between the Azores and Spain (maximum
values of approximately 4 and 3 hPa, respectively; Fig. 3a).
The model’s ensemble-mean EOF accounts for 43% of thevariance on average across the 40 ensemble members, andis largely similar to observations although the centers-ofaction extend slightly farther east and the southern lobe is
weaker (maximum amplitude of approximately 2 hPa compared to 3 hPa in observations; Fig. 3c). Note that although
the NAO is the single most dominant mode in both observations and the CESM-LE, other patterns of North Atlantic
circulation variability account for a little more than half ofthe total SLP variance.
The leading SLP EOF shows some variation in pattern and amplitude across the individual ensemble members, even though the statistics are based on such a long
period of record (1920–2012). For example, simulation
14 shows magnitudes that are nearly identical to observations (Fig. 3b), while simulation 25 shows a stronger
northern center-of-action (maximum amplitude around
5 hPa) and a weaker southern center (maximum amplitudeof approximately 1.5 hPa; Fig. 3d). In addition, the percent variance explained by the leading EOF varies acrossthe ensemble members, ranging from 32 to 49% (Fig. S5).
Thus, the characteristics of the interannual NAO may not
be precisely known with “only” 93 years of data, at leastaccording to the CESM-LE. As a corollary, the observed
NAO characteristics may also be subject to some uncertainty, and thus any individual simulation need not match
observations exactly. However, for the model to be realistic, its range of NAO patterns and amplitudes must span
the one “realization” from nature. By these measures, the
CESM-LE produces a credible NAO, given the length ofthe observational record available for assessment. The temporal characteristics of the model’s NAO will be considered below. The SAT and P anomalies associated with theleading interannual SLP EOF are also well simulated in the
CESM-LE (Fig. 3). In particular, a positive NAO is associated with positive SAT anomalies over Europe, Russia andthe eastern U.S., and negative SAT anomalies over northern
Africa, the Middle East, eastern Canada and Greenland inboth observations (Fig. 3a) and the ensemble-mean of themodel simulations (Fig. 3c). In addition to the realistic spatial pattern of SAT anomalies, the simulated amplitudes arealso in line with observations. However, as expected, theThe role of the North Atlantic Oscillation in European climate projections﻿	

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(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 3  Interannual NAO and associated SAT and precipitation anomalies. All panels show regressions upon the normalized leading PC
of winter SLP anomalies based on detrended data during 1920–2012.
SLP (contours; interval = 1 hPa with negative values dashed) andSAT (color shading; °C) from: a observations; b CESM1 simulation 14; c CESM1 ensemble mean; d CESM1 simulation 25. Stippledareas in panel (a) indicate where the observed SAT regression value
lies outside the 40 values simulated by the CESM1 Large Ensemble. Panels e–h as in a–d but for precipitation (mm ­day−1) in placeof SAT. Percent variance explained by the NAO PC indicated at theupper right of each panel

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magnitudes and spatial pattern of the SAT anomalies mayvary across the individual ensemble members, consistent
with the variation in the SLP anomalies. For example, simulation 25 shows larger SAT anomalies over central Europe(maximum values around 2–3 °C) compared to observations (maximum values around 1–2 °C; Fig. 3d). However,
the observed SAT anomalies lie within the range of values
simulated across the 40 ensemble members of the CESMLE at nearly all locations, as indicated by the lack of stippling in Fig. 3a. Exceptions occur over portions of the Middle East, central Russia and the southeastern United States.
The model’s ensemble-mean P anomalies exhibit arealistic dipole pattern, with the largest positive values (inexcess of 0.75 mm d­ ay−1) over northern Europe, especially
the west coast of Great Britain and Scandinavia, and largest negative values of comparable amplitude over southern
Europe, particularly Portugal, Spain, and other countries
bordering the Mediterranean Sea (compare Fig. 3e, g).
However, as with SAT, the magnitude of the P anomalies
varies across the ensemble. For example, simulation 25
depicts smaller precipitation reductions over the Mediterranean region compared to simulation 14, consistent withthe weaker SLP amplitude over the southern center-ofaction (Fig. 3f, h). In addition to magnitude variations, thedetails of the spatial pattern of P anomalies can differ fromone ensemble member to another: for example, Franceand Great Britain show negative P anomalies in simulation 14 and positive P anomalies in simulation 25. Thesevariations in the position of the nodal line and amplitude of
the P anomaly dipole associated with the NAO, which are
due entirely to sampling fluctuations even with 93 years ofrecord, has implications for paleo-climate reconstructions(Cook 2003).
The observed P regression values in areas most strongly
affected by the NAO, which includes all of western andEastern Europe and most of eastern North America, lie
within the spread of the 40 values simulated across the
CESM-LE (Fig. 3e). The observed P anomalies over northern Russia and much of North Africa lie outside of themodel spread.
The 5–95% range of simulated NAO regression values
across the 40 ensemble members is summarized in Fig. 4.
This range is constructed by computing the standard deviation (σ) of the 40 regression values at each grid box for

(a)

(b)

(c)

(d)

Fig. 4  The 5–95% range of interannual NAO regression values
across the 40 members of the CESM1 Large Ensemble based on
detrended data during 1920–2012. (a) and (b) show SLP (contours;
interval = 1 hPa with negative values dashed) and SAT (color shad-13

ing; °C) regressions associated with a −2 and +2 standard deviation
departure of the NAO, respectively. c, d as in a and b but for precipitation (mm ­day−1) in place of SAT. See text for details

The role of the North Atlantic Oscillation in European climate projections﻿	each variable (SLP, SAT and P) based on detrended data
during 1920–2012, and subtracting/adding these values(multiplied by two) from/to the ensemble mean regression value. Note that these regression maps are intended
to convey the 5–95% range of uncertainty in the simulated
interannual NAO regression values arising from sampling
fluctuations, and should not be interpreted as patterns
occurring in any individual simulation. There is a large
range of uncertainty in the SAT regression values overnorthern Europe and Russia (1–3 °C), and in the P regression values over western Europe (0.15–0.75 mm ­day−1).
This range is associated with uncertainty in the overlying
large-scale SLP regression pattern. In particular, a stronger
southern center-of-action of the NAO (i.e., a stronger highpressure anomaly) and a northward shift of the SLP dipole
nodal line is associated with greater precipitation deficits
over southern and central Europe; and a southward shift of
the nodal line accompanied by stronger anomalous westerly
flow across northern Europe and Russia favors enhanced
precipitation and warming in these regions (Fig. 4). This
range of uncertainty on the simulated NAO and its climate
impacts cautions against over-interpreting results based on“only” 93-years of data, be it from a model run or fromnature.
Up to now, we have focused on the spatial characteristics
of climate anomalies associated with historical NAO variability. It is also useful to examine the temporal characteristics of the NAO. As mentioned in the Introduction, theobserved NAO exhibits prominent low-frequency variability on time scales of decades and longer. Does the CESMLE show similar low-frequency fluctuations, and to what
extent are these forced? The NAO time series from observations and each of the first 30 members of the CESM-LE
over the period 1920–2012 are displayed in Fig. 5 (only the
first 30 members are shown to save space). A 10-year lowpass filter has been applied to each record to highlight decadal and longer time scales. While the individual ensemble
members exhibit realistic amplitudes of low-frequency variability, this variability is almost entirely internally generated as seen by the comparatively small magnitude of theforced response (given by the 40-member ensemble-meantime series shown in the lower right panel of Fig. 5). Given
that the model’s low-frequency NAO variability is almost
entirely internally-generated, and if the same is true forobservations, then the chronologies of the simulated andobserved NAO time series need not match. Indeed, theNAO shows a wide variety of temporal sequences acrossthe 30 members of the CESM-LE, with ensemble member
5 lining up with the observed record just by chance (Fig. 5).
It is important to note that we have focused only on
the low-pass filtered portion of the NAO time series.
The full NAO record exhibits energetic inter-annual fluctuations (not shown), and consequently there is very little

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autocorrelation in any of the simulated time series, consistent with observations (Fig. S5b). The 1-year lag autocorrelation of the detrended NAO timeseries during 1920–2012
ranges from −0.28 to +0.16 across the 40 members of
the CESM-LE, with an average of −0.05 (Fig. S5b). The
observed autocorrelation (+0.17 based on detrended data)lies just outside the high end of this range, but not significantly so. Note that the range of simulated autocorrelation
values is entirely due to sampling fluctuations associatedwith a short record: none are significantly different fromzero.
3.4 Using the statistics of interannual NAO variability
to infer the range of NAO trends
3.4.1 Application to CESM1
For a Gaussian time series, the margin of error on a trendof length Nt estimated by linear least-squares regression isa function of the magnitude of the interannual variability(given by the standard deviation σ), the lag-one autocorrelation and the trend length (Thompson et al. 2015). For thecase of zero autocorrelation, the two-tailed 95% margin oferror on a 30-year (50-year) trend is σ multiplied by 1.27
(0.98). The dependence of these values on the lag-one autocorrelation is given in Figure 2 of Thompson et al. (2015).
To see how well this relationship holds for the model’s
NAO in the absence of climate change, we make use of
the 2200-year coupled (CESM1) pre-industrial control
simulation described in Sect. 2.1. Specifically, we computethe leading EOF of detrended DJFM SLP anomalies overthe entire length of the run (e.g., the “interannual” EOF),
and also the leading EOF of 30-year trends formed fromsequential 30-year segments of the run, overlapping by
15 years to increase the sample size, for a total of 146 trend
samples. The corresponding PC records are then standardized and used to produce the SLP, SAT and P regression
maps. Finally, we multiply the interannual regression maps
by 1.27, the appropriate value for the 95% margin of error
on a 30-year trend given a 1-year lag autocorrelation of thePC timeseries of 0.01. The resulting scaled-interannual andtrend SLP regression maps closely resemble one another inboth pattern and magnitude (Fig. 6), indicating that in the
CESM control simulation the margin-of-error on a 30-year
trend in the NAO can be well estimated from the statisticsof the interannual variability of the NAO. The corresponding scaled-interannual and trend versions of the SAT and
P regression maps are also very similar to one another(Fig. 6). The slight overestimate of the scaled-interannual
SAT values over Russia and P values over Western Europe
compared to their trend counterparts is consistent with thelocally stronger gradients in the SLP field, which drivethese surface climate impacts.

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Fig. 5  Low-pass filtered NAO time series from each of the first 30
members of the CESM1 Large Ensemble (blue curves; numbers inupper left of each panel denote the ensemble member number) andfrom observations (gray curve in upper left panel, and repeated ineach subsequent panel) during the period 1920–2012. The lower rightpanel labeled “EM” shows the 40-member ensemble-mean NAO timeseries (blue), indicative of the forced response in CESM1. The vertical axes are identical for each panel

Repeating this exercise on the 2600-year atmosphericcontrol simulation produces nearly identical results to thecoupled control run (Fig. S6), indicating that the NAO
and its terrestrial climate impacts are primarily a resultof intrinsic atmospheric dynamics: i.e., this mode does
not rely on air-sea interaction for its existence or dominant characteristics. Whether the slight differences in SLP
amplitudes between the coupled and atmospheric control
simulations can be attributed to ocean coupling (either
local or remote) or to sampling fluctuations remains to beassessed.

Having shown that the scaling method works well in the
CESM control runs, we next ask: how well can the marginof-error on future trends in the NAO be inferred from thestatistics of present-day interannual NAO variability? Ifthey can be inferred to a reasonable degree, then one canuse the observed characteristics of interannual NAO variability to estimate the error on future NAO trends, rather
than relying solely on the model. We address this question
using the CESM-LE as a test bed.
Figure 7 compares the scaled-interannual regressions based on the historical period (1920–2014) with the13The role of the North Atlantic Oscillation in European climate projections﻿	

3151

(a)

(b)

(c)

(d)

Fig. 6  Comparison between the NAO and associated climate impacts
in the 2200-year CESM1 control integration computed from interannual data (scaled as described in the text) and from 30-year trends.
a Scaled-interannual regressions of winter SLP (contours) and SAT(color shading) anomalies upon the normalized leading PC of winterSLP anomalies; b SLP and SAT trend regressions upon the normal-

ized leading PC of winter SLP trends; c As in (a) but for precipitationin place of SAT; d As in (b) but for precipitation in place of SAT.
SAT in units of °C per 30 years, precipitation in units of mm d­ ay−1
per 30 years, and SLP contour interval of 1 hPa per 30 years withnegative values dashedregressions based on future (2016–2045) trends. Here,
we have applied the interannual scaling factor appropriate to each individual ensemble member (autocorrelation
values shown in Fig. S5b) and then averaged the resulting 40 scaled-interannual regression maps to produce theensemble-mean results shown in Fig. 7a, c. Nearly identicalresults are obtained using the scaling factor appropriate forthe average 1-year lag autocorrelation across the ensemble(−0.05) to each member, or the scaling factor appropriate
for zero autocorrelation to each member (not shown). It is
worth noting that the average interannual characteristics ofthe model’s NAO and associated SAT and P impacts do notchange appreciably between the pre-industrial period (asgiven by the coupled control simulation: recall Fig. 6a, c)and the historical (Fig. 5a, c) or future (2016–2045: Figs.
S7a and c) segments of the CESM-LE. That is, the dominant mode of winter atmospheric circulation variability in
the Atlantic/European sector in the CESM is largely unaffected by climate change occurring between 1850 and
2045.

It is evident from Fig. 7 that the model’s scaled-interannual regressions based on the historical period and thetrend regressions based on the next 30 years are largely
similar in structure, with some regional differences inamplitude. In particular, the SLP trend regressions show
greater magnitudes over the central North Atlantic and aslight northward shift in the nodal line compared to theirscaled-interannual counterparts. These differences in SLP
are reflected in the SAT and P fields, with more pronounced
cooling and drying over southern Europe in the trends compared to the scaled-interannual fields. In addition, Western
Russia exhibits greater warming in the trend regressions
than the scaled-interannual regressions, with maximum
values around 3–4 °C compared to 2–3 °C. These regional
differences in amplitude notwithstanding, the large-scalefeatures of the SLP, SAT and P regression maps show astrong degree of resemblance between the version basedon historical scaled-interannual statistics and that based on
future 30-year trends. The comparison between the historical scaled-interannual and future trend regression maps is

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(a)

(b)

(c)

(d)

(e)

(f)

Fig. 7  As in Fig. 6 but using the CESM1 Large Ensemble. a Ensemble-mean of scaled-interannual regressions of winter SLP (contours)
and SAT (color shading) anomalies upon the normalized leading PC
of winter SLP anomalies during 1920–2012; b SLP and SAT trend
regressions upon the normalized leading PC of winter SLP 30-year
trends based on 2016–2045; c as in (a) but for precipitation in placeof SAT; d as in (b) but for precipitation in place of SAT. SAT in units

of °C per 30 years, precipitation in units of mm ­day−1 per 30 years,
and SLP contour interval of 1 hPa per 30 years with negative values dashed. e Histogram of the NAO trend PC (gray bars) and the
expected distribution based on the interannual statistics of the NAO
during 1920–2012 (blue curve); f as in (e) but after smoothing thetrend histogram with a 3-point boxcar filter. See text for details

even closer when considering trends over the next 40 years(Fig. S8) and the next 50 years (Fig. S9).
The reasons for the regional differences in historicalscaled-interannual and future 30-year trend regressions areunclear, since as noted above the model’s interannual NAO
variability does not appear to be affected by climate change
between 1850 and 2045. Sampling variability may accountfor some, but not all, of the regional SLP differences, asevidenced by the range of results obtained from random
sub-samples of the CESM-LE members (not shown). The

fact that the 40-year (2016–2055) and 50-year (2016–2065)
SLP trend regressions do not differ significantly from theirscaled-interannual counterparts suggests that there may
be an additional source of multi-decadal variability that is
influencing the statistics of simulated NAO trends over the
next 30 years. One candidate is the ocean’s Atlantic Meridional Overturning Circulation (AMOC), which shows anapproximate 60-year periodicity in the majority of CESMLE ensemble members in both the historical (1920–2012)
and future (2012–2100) segments of the simulations,

13

The role of the North Atlantic Oscillation in European climate projections﻿	

accompanied by a weak influence on the NAO and sea ice
concentrations in the Barents and Kara Seas (not shown,
but see related results in Yeager et al. 2015; Delworth
and Zeng 2016). The latter may explain the differences in
SAT amplitudes over western Russia between the scaledinterannual and future 30-year trend regressions, as sea ice
amounts are known to influence terrestrial SATs adjacent to
the Arctic Ocean (i.e., Sun et al. 2015; Deser et al. 2016).
Further investigation of these issues is clearly warranted
but is beyond the scope of the present study. Additional
information on the frequency-dependent interplay between
AMOC and the NAO is provided in the idealized modelingstudy of Delworth and Zeng (2016).
As a last metric of the degree to which the scaling argument of Thompson et al. (2015) applies to the NAO in the
CESM-LE, we compare the histogram of the 40 NAO trend(2016–2045) PC values with that of a Gaussian time series
whose σ is computed from the interannual statistics of thedetrended NAO during 1920–2012, multiplied by 1.27
(Fig. 7e). Here, σ is computed for each ensemble member individually and then averaged over all 40 members,
but nearly identical results are obtained by appending thedetrended NAO time series from all 40 ensemble members
into one long record and then computing σ (not shown).
The trend and scaled-interannual NAO pdfs show remarkable agreement, especially when the noisy NAO trend histogram (which is based on only 40 values) is smoothedslightly with a 3-point boxcar filter (Fig. 7f). This agreement lends additional support to the applicability of theThompson et al. (2015) scaling argument to the CESM’s
NAO.
In summary, our results show that in the CESM-LE, therange of uncertainty in projected NAO trends and associated influences on SAT and P over the next 30 years canbe obtained to a large degree from the Gaussian statistics of NAO variability during the historical period, withsome regional exceptions possibly associated with AMOC
variability.
3.4.2 Application to observations
Next, we use the observed characteristics of presentday interannual NAO variability to estimate the error on
future NAO SLP trends and associated SAT and P trends.
Here, it must be borne in mind that the 93-year sample(1920–2012) of SLP observations may not be adequate
for precisely estimating the statistics of the NAO, given
the member-to-member variability in the statistics derived
from the CESM-LE (recall Fig. 3). For this calculation,
we scale the observed interannual SLP, SAT and P regression values by the factor 1.53 appropriate for a 30-year
trend and an observed NAO autocorrelation of 0.17 basedon detrended data during 1920–2012 (Fig. S5b). Then we3153

add/subtract this scaled interannual regression map to/fromthe anthropogenically-forced component of the trend overthe next 30 years, the latter estimated from the ensemblemean of the CESM-LE (Fig. 8) or the ensemble-mean of
the 38 CMIP5 models (Fig. 9). These NAO “book-ends”
provide an estimate of the 5–95% range of uncertainty inprojected trends due to internal variability of the NAO
based on observations superimposed upon model estimatesof human-induced climate change.
It is clear from Figs. 8 and 9 that there is a broad rangeof uncertainty on NAO-related SLP trends over the next
30 years due to sampling fluctuations derived from thestatistics of the observed NAO, with both positive andnegative NAO trends possible. This indicates that internalvariability will dominate over the forced response for NAO
trends over the next 30 years, regardless of whether theforced response is estimated from the ensemble-mean of
the CESM-LE or the CMIP5 models. Further, this range of
uncertainty on the NAO-related SLP trends has substantial
consequences for both SAT and P trends. In particular, P
trends in northern Europe (Scandinavia and Scotland) andin countries bordering the Mediterranean Sea can change
sign, while SAT trends over northern Europe and Russia can range from near zero or even slight cooling to over
4 °C, depending on the polarity of the 2σ NAO trend. The
similarity of these observationally-based “NAO book-end”
trend maps with those derived directly from the leading
EOF of the set of 40 CESM-LE SLP trend maps (Fig. 2)
attests to the robustness of the results, the utility of themethod of Thompson et al. (2015) to estimate uncertainty
in trends from the statistics of a Gaussian time series, andthe fidelity of CESM’s simulation of the NAO.
We have applied the same procedure to estimate the
5–95% range of uncertainty on trends over the next
50 years (2016–2065). The results indicate that the sign of
NAO-related SLP trends remains uncertain over the next
50 years (Figs. S10 and S11). However, the opposing circulation trends have a smaller impact on the range of SAT and
P trends due to the fact that the radiatively-forced responseis greater over the longer (50-year) time horizon. Thus,
over the next 50 years, SAT is expected to warm everywhere, and P is expected to increase over most of northern
Europe, western Russia and eastern North America, regardless of the polarity of the NAO trend (Figs. S10 and S11).
However, the magnitude of the warming and the sign of the
P trends over southern Europe still depend on the sign of
the NAO trend over the next 50 years.
3.5 Chance of a positive SAT or P trend
in the CESM‑LE
We conclude by summarizing the impact of internal variability on future climate trends by showing maps of the

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(a)

(b)

(c)

(d)

Fig. 8  Impact of the NAO on future 30-year climate trends (2016–2045) based on interannual statistics of the NAO from observations
and the forced response from the CESM1 Large Ensemble. a, b
show the expected range of future SLP and SAT trends; c, d show

the expected range of future SLP and precipitation trends. See text
for details. SLP contour interval is 1 hPa per 30 years with negativevalues dashed; SAT (color shading) in units of °C per 30 years; andprecipitation (color shading) in units of mm ­day−1 per 30 years

chance of a positive trend in SAT and P over the next
30 years (Fig. 10a, b, respectively) and the next 50 years(Fig. 10c, d, respectively) according to CESM1. These
probabilities are obtained by dividing the number of
CESM-LE ensemble members that show a positive trend
by the total number of ensemble members (40) at eachlocation. Similar maps were shown in Deser et al. (2014)
for North America, based on the CCSM3 40-member
ensemble for trends over approximately the next 50 years.
Note that this calculation incorporates all sources of internal variability, not just that due to the NAO.
Winter SAT is virtually guaranteed to warm over the
next 30 years (e.g., chance of a positive trend exceeds95%) everywhere except Greenland, northern Europe
and Russia where the risk is slightly lower (75–95%;
Fig. 10a). The near future is less conclusive with regard
to winter P, with many locations including southern andcentral Europe, Iran, and Kazakhstan showing nearly
even chances for increased or decreased P over the next
30 years, and most regions showing values between 35

and 65% (Fig. 10b). The most definitive 30-year P trends
occur along the northern Russian border and adjacent to
Hudson’s Bay (>75% change of a wetter future), likely
in response to diminished sea ice cover and resulting
increase in atmospheric moisture, and in some areas ofnorthern Africa and the Middle East (<35% chance ofwetting, equivalent to >65% chance of drying; Fig. 8b).
More robust trends are indicated for the next 50 years,
with all locations showing >95% chance of warming(Fig. 10c) and a >85% chance of increasing P over northern Europe and western Russia as well as most of eastern
North America, and a >85% chance of drying over northwestern Africa and regions directly adjacent to the Mediterranean Sea (Fig. 10d). Note that the sign of precipitation trends in areas most directly impacted by the NAO
such as southern Europe and the west coasts of Norway,
the U.K. and Iceland, remains uncertain even for the next
50 years (Fig. 10d) consistent with the results shown in
Figs. S10 and S11.

13

The role of the North Atlantic Oscillation in European climate projections﻿	

3155

(a)

(b)

(c)

(d)

Fig. 9  As in Fig. 8 but using the CMIP5 multi-model mean in place of the CESM1 Large Ensemble for the forced trend

4 Summary and discussion
This study has highlighted the role of internal variabilityof the NAO, the leading mode of atmospheric circulation variability over the Atlantic/European sector, on winter (December-March) surface air temperature (SAT) and
precipitation (P) trends over the next 30 years (and thenext 50 years: see Supplemental Materials) using a new
40-member ensemble of climate change simulations with
CESM1. Each member is subject to the identical increasein GHG, but starts from a slightly different initial atmospheric state in 1920. Thus, any spread in trends across theensemble is a measure of the relative importance of unpredictable natural variability and forced climate change.
Because the NAO is primarily controlled by intrinsic
atmospheric dynamics, it constitutes a major source of
unpredictable natural variability whose impacts will besuperimposed upon those of anthropogenic climate change.
Thus, future climate trends in regions affected by the NAO
are best conveyed in terms of an expected range that incorporates both the natural variability and the forced climatechange signal. Our results show that this expected range
resulting from internal variability of the NAO is substantial
for both SAT and P trends over the next 30 years, and in

the case of P can even change the sign of the trend. While
the NAO’s impacts on SAT and P trends over the next
50 years are smaller, they remain important for assessing
the magnitude of future warming and precipitation change.
Further, the large-scale imprint of the NAO on surface climate imparts spatial coherence to this leading source of
uncertainty in future climate trends, with implications for
agricultural and water resources. Although the NAO is thedominant pattern of atmospheric circulation variability,
accounting for about half of the total winter SLP varianceon both interannual and multi-decadal time scales, otherlarge-scale structures of internal circulation variability will
also undoubtedly contribute to uncertainty in future climate
trends.
While the statistics of 30-year (or longer) NAO trends
and associated surface climate impacts cannot be reliably
determined from the short observational record, we havemade use of a simple relationship between the statisticsof trends of any length and the statistics of the interannual
variability, provided the time series is Gaussian (Thompson et al. 2015). This relationship, which we show holds
to a large extent in the CESM-LE and in the long CESM
coupled and atmospheric control simulations, has enabled
us to use the observed interannual NAO statistics to infer

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(a)

(b)

(c)

(d)

Fig. 10  Percent chance of a positive 30-year trend (2016–2045) inwinter (a) SAT and (b) precipitation from the CESM1 Large Ensemble. c As in (a) but for a 50-year trend (2016–2065); d as in (b) but

for a 50-year trend (2016–2065). Note that for precipitation, a small
chance of a positive trend (moistening) is equivalent to a high chanceof a negative trend (drying). Color bar is in units of %the magnitude of 30-year (and 50-year) NAO trends thatwill be superimposed upon anthropogenic climate change.
We find that the expected 95% range of future climate
trends induced by NAO fluctuations estimated from theobserved statistics of the NAO and the modeled response toincreased GHGs is largely similar to that obtained from the
CESM-LE directly, attesting to the fidelity of the model’s
representation of the NAO and the utility of this approach.
We note further that even with nearly 100 years of data, theinterannual statistics of the NAO are subject to considerable uncertainty, including its standard deviation and autocorrelation, with consequences for impacts on SAT and
P. Thus, one might argue that a large ensemble of climatechange simulations with a given model still provides useful guidance to the range of future climate trends expectedfrom the superposition of natural variability and forced climate change.

Open Access This article is distributed under the terms of theCreative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide alink to the Creative Commons license, and indicate if changes weremade.

Acknowledgements We thank the Reviewers for their thoughtfulcomments and suggestions. We also appreciate discussions with Drs.
Isla Simpson and Elizabeth Barnes during the course of this work.
We acknowledge all the contributors to the CESM Large Ensemble
Community Project (http://www.cesm.ucar.edu/projects/communityprojects/LENS/) and supercomputing resources for the Large Ensemble provided by NSF/CISL/Yellowstone. NCAR is sponsored by the
National Science Foundation.

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