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Modeled and Observed Multidecadal Variability in the North Atlantic Jet
Stream and Its Connection to Sea Surface Temperatures
ISLA R. SIMPSON AND CLARA DESER
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado

KAREN A. MCKINNON
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado,and Descartes Labs, Santa Fe, New Mexico

ELIZABETH A. BARNES
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado(Manuscript received 22 March 2018, in final form 8 June 2018)
ABSTRACTMultidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is comparedwith that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within thewinter months, with greatly enhanced variability toward the late winter. This late winter variability exceedsthat found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating
the need for an underlying explanation. The potential roles of both external forcings and internal coupled
ocean–atmosphere processes are considered. While the late winter variability is not found to be closely
connected with external forcing, it is found to be strongly related to the internally generated component ofAtlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of theseasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly
connected to jet stream variability during March than the early winter months or the winter season as a whole.
Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the
SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch
between late winter jet stream variability in observations and GCMs and a potential source of long-term
predictability of the late winter Atlantic atmospheric circulation.

1. Introduction
The North Atlantic is a region characterized bysubstantial variability in the circulation of both theatmosphere and ocean on a wide range of time scales.
The highly populated surrounding continents of North
America and Europe, as well as North Africa andGreenland, are impacted by this variability as it manifests in fluctuations in the characteristics of weather
patterns, with impacts on storminess, temperature, andprecipitation. This is particularly true during the wintertime when the North Atlantic storm track is at its mostactive and its variability is most pronounced (Hurrell
Corresponding author: Isla R. Simpson, islas@ucar.edu

et al. 2003). Accurate prediction of the climate in theseregions using general circulation models (GCMs), andan appreciation of its uncertainties, whether it be on
subseasonal-to-seasonal, decadal, or centennial time scales,
requires an accurate representation of both forced andunforced variability in the atmosphere, the ocean, and thecoupling between them. As a result of the wide range ofcomplex processes that contribute to the climate of theNorth Atlantic and its variability, this continues to be achallenge (e.g., Woollings 2010).
One aspect where GCMs have recently been shown to
exhibit a deficiency is in their decadal to multidecadal
variability in the North Atlantic jet stream, as viewed
through the North Atlantic Oscillation (NAO). The
NAO is the dominant mode of variability in North

DOI: 10.1175/JCLI-D-18-0168.1
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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Atlantic sea level pressure (SLP) (e.g., Walker and Bliss
1932; van Loon and Rogers 1978; Wallace and Gutzler
1981; Barnston and Livezey 1987; Hurrell and Deser
2010; Woollings et al. 2015); while it is not the only modeof variability in the North Atlantic jet stream (e.g.,Athanasiadis et al. 2010), much of the research on North
Atlantic atmospheric variability has centered around it.
Hurrell (1995) demonstrated pronounced decadal tomultidecadal time scale fluctuations in the NAO during
the last century, with associated fluctuations in regionaltemperatures and precipitation over northern Europe,
the Mediterranean, North America, and Greenland(Hurrell and van Loon 1997). This sparked debate as towhether such multidecadal fluctuations should be taken
as evidence of multidecadal time scales in the atmosphere, with a particular underlying cause, or whether
they might simply arise from the sampling of highfrequency white noise or weakly red noise processes(i.e., the averaging of atmospheric noise with no need to
invoke an underlying low-frequency forcing) (Wunsch
1999; Stephenson et al. 2000; Feldstein 2000). With regard to the long-term positive NAO trend that was observed from the 1950s to 1990s (Hurrell 1995), while thesampling of high-frequency atmospheric noise almost
certainly contributes, studies have argued for a role for
forcing from increasing greenhouse gas concentrations(Gillett et al. 2003) and from sea surface temperature(SST) variability in the extratropics (Rodwell et al.
1999) or in the tropics (Hoerling et al. 2001; Hurrell et al.
2004). While some studies have shown that the magnitude of the long-term NAO trend seen over the latter
half of the century is reproducible in models and consistent with internal ocean–atmosphere variability (Selten
et al. 2004; Raible et al. 2005; Deser and Phillips 2009),
models generally fail to capture the amplitude of theshorter-term trend from the mid-1960s to the mid1990s even when they are run with prescribed historical SSTs and forced with time-evolving greenhousegas and aerosol concentrations (Scaife et al. 2009; Kimet al. 2018).
While the debate over the underlying reasons for thismultidecadal variability in the North Atlantic jet stream
continues, it appears that the current generation of
GCMs does not exhibit the same degree of multidecadal
North Atlantic jet stream variability as has been observed over the twentieth century. Kravtsov (2017) and
Wang et al. (2017) provided a comparison of winter andannual mean NAO variability on different time scales
between observations/reanalysis products and models
from phase 5 of the Coupled Model Intercomparison
Project (CMIP5; Taylor et al. 2012). They each foundthat interannual NAO variability was simulated withfidelity but almost all models were deficient in their

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representation of NAO variability on longer time scales.
Similar conclusions were reached by Kim et al. (2018)using the Community Earth System Model, version 1(CESM1; Hurrell et al. 2013). They further argued
that the deficient multidecadal NAO variability may
be a cause of reduced variability in the ocean’s Atlantic meridional overturning circulation (AMOC) andassociated SSTs.
Given the importance of the North Atlantic jet stream
for the climate of the surrounding continents as well as
its potential global impact through the ocean circulation,
it is important that models simulate its variability withfidelity. However, our understanding of the reasons
behind the model deficiencies cited above, remains
limited. One, or a combination, of the following threepossibilities is likely responsible:
1) The observed multidecadal variability does indeed
result from the averaging of atmospheric noise (e.g.,
Wunsch 1999; Feldstein 2000) and either the observed sequence of atmospheric noise was unlikely,
or models are deficient in their representation of thisatmospheric noise.
2) The observed multidecadal variability has been externally forced by factors such as changing greenhousegas (GHG) or aerosol concentrations, volcanic eruptions, or solar variability, and either (i) the forcing
datasets used to drive our models are in error or (ii) themodels do not correctly simulate the response to these
forcings. These forcings could either act directly on the
North Atlantic jet stream or indirectly through theirinfluence on the ocean or both.
3) The observed multidecadal variability arises frominternal coupled ocean–atmosphere processes. Thatis, the long time scales inherent to the ocean are responsible for the long time scale jet variability seenin the observations, and models are either deficient intheir representation of the important ocean processes,
the ocean–atmosphere coupling, or both.
In this study, we elucidate some nuanced aspects of theobserved multidecadal variability in the wintertime North
Atlantic jet stream and its representation in models. We
offer a slightly different view of this issue from previous
studies by not limiting ourselves solely to the NAO andby considering the additional seasonality within the winter months. While, as will be discussed, our capacity forgaining a complete mechanistic understanding of thevariability (either through the use of models or via theobserved record before the satellite era) is limited, ouranalysis does lead us to conjecture that possibility 3 islikely playing an important role. That is, the observed
multidecadal variability arises from internal coupled
ocean–atmosphere processes and models are deficient15 OCTOBER 2018

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in their representation of these processes. This will,
however, only be proven correct (or otherwise) as either our models improve or our observational record
lengthens.
The observation- and model-based datasets andmethods used are described in section 2. The variability incoupled models is then compared with that in observations in section 3, where it is shown that the observed
variability is considerably greater than typically found in
GCMs during the late winter. The nature of the variability
that has been observed in terms of Atlantic jet structure isthen briefly described in section 4 before its linkage with
SST variability and external forcings is assessed in section 5. Discussions and conclusions are then provided in
section 6.

2. Observation-based datasets, model simulations,and methods
a. Reanalyses
Our focus on decadal to multidecadal time scale variability necessitates the use of observation-based datasets that are of a sufficient length. We therefore use thereanalysis products that cover the entire twentieth century, namely the European Centre for Medium-Range
Weather Forecasts (ECMWF) Twentieth Century Reanalysis (ERA20C; Poli et al. 2016), which is available
from 1900 to 2010, and the National Oceanic and Atmospheric Administration’s (NOAA’s) Twentieth Century
Reanalysis (20CR; Compo et al. 2011), which is available
from 1850 to 2014. Only surface pressure observations
are assimilated in 20CR while additional observationsof marine surface winds are assimilated in ERA20C. Of
these two datasets, our primary focus will be on ERA20C
although the conclusions do not differ from 20CR. These
will also be compared with the shorter reanalyses over
the recent decades that assimilate a wider range of observations: ERA-Interim from 1979 to 2017 (Dee et al.
2011), MERRA2 from 1980 to 2017 (Gelaro et al. 2017),
JRA-55 from 1958 to 2016 (Kobayashi et al. 2015), andERA-40 from 1958 to 2001 (Uppala et al. 2005).

b. SST datasets and indicesThree SST datasets will be used to assess the linkage
between Atlantic jet stream variability and SST variability: NOAA’s Extended Reconstruction SSTs versions 3b and 5 [ERSSTv3b (Smith et al. 2008) andERSSTv5 (Huang et al. 2017)], both of which areavailable from 1854 to 2017, along with the Hadley
Centre Global Sea Ice and Sea Surface Temperaturedataset (HadISST; Rayner et al. 2003) which is available
from 1870 to 2016.

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We make use of an index of area averaged SST from
808W to 08 and from the equator to 608N and refer to thisas the Atlantic multidecadal variability (AMV) index.
A variety of methods have been used to isolate the internal variability component of AMV from the externally forced component (Trenberth and Shea 2006; Ting
et al. 2009; Frankcombe et al. 2015; Frankignoul et al.
2017; Murphy et al. 2017) and here we make use of threeof them: the Trenberth and Shea (2006) method (theTS method), the linear inverse model (LIM) optimal
method of Frankignoul et al. (2017) (the LIMoptimal
method), and a simple subtraction of a linear trend following Murphy et al. (2017) (the Linear method). The
first two methods, TS and LIMoptimal, are designed toremove the contribution of all external forcings andFrankignoul et al. (2017) demonstrated their success indoing so in models in the North Atlantic, particularly for
the LIMoptimal method. On the other hand, the Linear
method does not successfully remove the entire externally forced component (Tandon and Kushner 2015;
Frankignoul et al. 2017) but we are motivated to consider it following Murphy et al. (2017), who argue thatnatural and anthropogenic aerosol forcing may havebeen an important driver of past AMV and that thisindex retains that component of forced variability.
For the TS method, the North Atlantic SST anomalies
are taken relative to the average, from 608S to 608N over
all ocean basins. For the LIMoptimal method the forced
component of North Atlantic SST variability has been
removed based on a LIM estimate with an optimal
perturbation filter prior to taking the North Atlantic
average [see Frankignoul et al. (2017) for more details].
This LIMoptimal estimate of internal SST variability has been provided in 3-month averages (January–
March, etc.) for 1900–2015 for ERSSTv5 and HadISST(G. Gastineau 2017, personal communication). Finally,
for the Linear method the linear trend over the length of
the record for the given SST dataset was subtracted fromeach ocean grid point before calculating the North Atlantic average.
These indices are low-pass filtered by taking a runningmean, typically of 20-yr length, but we emphasize thatthe pattern of SST variability that it represents doeschange with the time scale considered by showing the
SST patterns that accompany the TS AMV index in
Fig. 1. Interannually, it depicts a horseshoe pattern inNorth Atlantic SSTs with a positive anomaly in the
subpolar gyre and an accompanying positive southernlobe (Fig. 1a). It is this horseshoe pattern that is typicallyreferred to as the Atlantic multidecadal oscillation(AMO) (e.g., Kushnir 1994; Enfield et al. 2001; Trenberthand Shea 2006), and it has been argued that the direct
forcing from North Atlantic wind variability plays an

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negative anomalies over much of the low-latitude North
Atlantic (Fig. 1c). The accompanying negative anomalies
in the Southern Hemisphere (SH) also intensify as progressively longer time scales are considered. The more
localized anomaly in the subpolar gyre region has beenargued as evidence of an impact of anomalous heattransports by the ocean circulation on these longer timescales (Delworth et al. 2017). Since our focus will primarily be on 20-yr running means, it is the global patternwith a locally intense anomaly in SST in the subpolar
gyre and oppositely signed anomalies in the SH (Fig. 1c)that our AMV index will be describing.

c. Model simulations
1) CESM SIMULATIONS

FIG. 1. ERSSTv5 DJFM SSTs regressed onto the DJFM
Trenberth and Shea (2006) AMV index for (a) interannual variability, (b) 10-yr running means, and (c) 20-yr running means. The
green box depicts the region used to define the AMV index.

important role in producing it (Clement et al. 2015; Caneet al. 2017). With 10-yr running means, the southern lobeof the horseshoe is weaker (Fig. 1b), and it has been argued that on these time scales, ocean dynamics playsa central role in AMV (Zhang et al. 2016). With 20-yr
running means the southern lobe is actually replaced by

A 40-member ensemble of coupled simulations areavailable through the CESM Large Ensemble (LENS)
project (Kay et al. 2015). These make use of the fully
coupled CESM, version 1 with the Community Atmosphere Model, version 5 (CESM1-CAM5; Hurrell et al.
2013) at approximately 18 horizontal resolution in theocean and atmosphere. Each member begins in 1920
and is forced with the CMIP5 historical forcings until
2005 and the representative concentration pathway
8.5 (RCP8.5) scenario thereafter. The first member was
branched from a preindustrial control simulation, representative of conditions in the 1850s. The remaining
39 members were then branched from year 1920 of thisinitial simulation with an additional round-off level
perturbation to the surface air temperature field toinitiate a different evolution of the climate system due
to internal variability. The accompanying 1200-yr-longcoupled preindustrial control simulation (piControl)with forcings that are representative of year 1850 will
also be used.
We also complement these free-running coupled simulations with initialized hindcasts. A 40-member ensemble of coupled decadal prediction simulations initialized
from 1 November each year from 1954 to 2015 with thesame version of CESM have been made available throughthe CESM decadal prediction large ensemble project(Yeager et al. 2018). These are run with the same forcings as LENS but with ocean and sea ice initial conditions obtained from a reanalysis-forced simulation withthe ocean and sea ice models while the atmosphere initial conditions are those of the 40 members of the freerunning LENS. These, therefore, represent ocean andsea ice initialized complements of the free-running LENS
simulations.
To assess the importance of the historical evolutionof SSTs, we make use of a 10-member ‘‘AMIP’’ ensemble with the same model version but with prescribed15 OCTOBER 2018

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time-evolving observed SSTs and sea ice concentrations
from ERSSTv4 (Huang et al. 2015). As with the coupled
historical simulations these are run under the CMIP5
historical and RCP8.5 (after 2005) forcing scenarios andthey extend from 1900 to 2009.

2) OTHER MODEL SIMULATIONS
The piControl simulation and each available historicalmember (from 1900 to 2005) for 35 models that participated in CMIP5 (listed in Table 1) will also be analyzed.
Equivalents to the CESM AMIP simulations of sufficient
length are not available through CMIP5, but we make useof an equivalent 10-member ensemble with the ECMWF
model (the underlying model of ERA20C reanalysis),known as the ERA20CM simulations (Hersbach et al.
2015), and refer to them as ECMWF AMIP. These extend
from 1900 to 2010 under CMIP5 forcings with prescribed
SSTs and sea ice from HadISST2.

d. Methods
With our focus being the eddy-driven jet stream in
the North Atlantic, the primary field of interest will be
700-hPa zonal wind (U700). Before comparing the variability between the reanalyses and the models, eachdataset is first interpolated onto a 28 3 28 longitude–
latitude grid using a cubic spline interpolation and thenisotropically smoothed in the spectral domain retaining
only scales larger than total wavenumber 42 according
to Sardeshmukh and Hoskins [1984, their Eq. (9) withno 5 42 and r 5 1]. This ensures the same spatial scales
are represented in each dataset, thereby removing any
potential grid dependency. The same procedure wasfollowed prior to the analysis of SLP variability discussed in appendix A. The various datasets are all ofdifferent lengths. For any comparison, the maximum
period of overlap for the datasets being considered will
be used, with the minimum length being 91 years. The
time period considered is, therefore, not entirely consistent throughout the analysis but results are unaffectedby the exact time period used.
The primary quantity used to characterize multidecadal variability will be the standard deviation s ofrunning means (typically 20-yr means) of monthly orseasonal averaged U700 at grid points in the Atlantic.
The extent to which this variability exceeds expectations
from the sampling of white noise year-to-year variability
will be assessed by comparison with the percentiles ofthe distribution of s values obtained from the runningmeans of 1000 synthetic time series of an equivalent
length (and ensemble size in the case of the LENS),
generated from Gaussian white noise with a standard
deviation equal to the interannual standard deviation
at a given grid point for the given month or season. A

TABLE 1. List of the number of historical members (from 1900–
2005) and the number of years in the piControl simulation used foreach model. (Expansions of acronyms are available online at http://
www.ametsoc.org/PubsAcronymList.)

Model

Historicalmembers

piControl
length (yr)

ACCESS1.0
ACCESS1.3
BCC-CSM1.1
BCC-CSM1.1-M
BNU-ESM
CanESM2
CCSM4
CESM1-CAM5
CESM1-WACCM
CMCC-CM
CMCC-CMS
CNRM-CM5CSIRO-Mk3.6.0
FGOALS-g2
FIO-ESMGFDL-CM3
GFDL-ESM2G
GFDL-ESM2M
GISS-E2-H
GISS-E2-R
HadGEM2-AO
HadGEM2-CC
HadGEM2-ES
INM-CM4
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC5
MIROC-ESM
MIROC-ESM-CHEM
MPI-ESM-LR
MPI-ESM-MR
MRI-CGCM3
NorESM1-M
NorESM1-ME

3
3
3
3
1
5
6
3
1
1
1
10
10
5
3
5
1
1
5
6
1
1
5
1
6
3
1
5
3
1
3
3
3
3
1

500
500
500
400
559
996
1051
319
200
330
500
850
500
700
800
500
500
500
780
850
700
240
576
500
1000
300
300
670
630
255
1000
1000
500
501
252

Kolmogorov–Smirnov test on the observed U700 confirms that a Gaussian distribution is a reasonable representation of U700 interannual variability (not shown).
When examining the link between U700 variability and
SSTs/AMV, the Pearson’s correlation coefficient will be
evaluated. To assess the significance of a given correlation,
the resampling methodology of Delworth et al. (2017)
will be employed, which allows for a suitable assessmentof significance with strongly autocorrelated time series. To
assess the significance of the correlation between two timeseries TS1 and TS2 of length N, this procedure works asfollows: TS1 is shuffled by obtaining a random number i
between 1 and N and piecing together the segment fromyear 5 i to year 5 N with the segment from year 5 1 toyear 5 i 2 1. This is repeated for TS2 with a different
random value i, and the correlation between these two

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FIG. 2. U700 standard deviations s of 20-yr running means for (a),(f),(k) the DJFM season, (b),(g),(l) December, (c),(h),(m) January,(d),(i),(n) February, and (e),(j),(o) March. (top) ERA20C reanalysis from 1920–2010, (middle) the CESM LENS from 1920–2010, and(bottom) the difference between ERA20C and LENS. The stippling in (a)–(j) indicates regions where the variability is significantly
different from white noise at 5% (gray) and 1% (white) levels. Spatial correlation has been accounted for by increasing the thresholdpercentile value for significance by the false discovery rate method of Wilks [2016, their Eq. (3)] with a false discovery rate control value of
20%. Gray regions in (k)–(o) indicate where ERA20C lies within the LENS ensemble spread. The green box in (e) shows the region used
for the U700NA index in subsequent figures. Only 1920–2010 is used for ERA20C so that equivalent record lengths are used in ERA20C
and the LENS historical members for the shading in (k)–(o) but (a)–(e) are very similar when the full record is used.

shuffled time series is assessed. This is done 10 000 times
with different values of i to build up a distribution of correlations that could occur between these time series bychance. Significance will be assessed by a two-sided test
(i.e., for significance at the 5% level, we assess whether
the correlation is greater than the 97.5 or less than the
2.5 percentile value of the 10 000 samples).

3. A comparison of multidecadal variability inNorth Atlantic zonal wind between models andreanalyses
Multidecadal variability in U700 is depicted on a grid
point basis for ERA20C and CESM in Fig. 2 by showing
the s of 20-yr running means from 1920 to 2010 for the

December–March average and each of the individual
winter months. For ERA20C the variability increases
throughout the winter season, strongly maximizing inMarch (Fig. 2e), whereas for CESM LENS there is no apparent seasonality within the winter months (Figs. 2g–j).
A comparison of the variability in the LENS with that in
ERA20C (Figs. 2k–o) reveals that, in the later portionof the winter (March and to a lesser extent February),
the variability in ERA20C is considerably greater thanthe mean variability calculated from the individual LENS
members and is, in fact, greater than estimated fromany individual LENS member. This March variability
contributes to the December–March (DJFM) differences between CESM LENS and ERA20C, particularly
to the west of the United Kingdom (Fig. 2k). However, it15 OCTOBER 2018

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should be noted that even if March were excluded and the
DJF average considered instead, there are still significant
differences between CESM LENS and ERA20C to theeast of Newfoundland and in the South Atlantic becauseof the contributions from January and February. Notealso that the lack of structure in the CESM variability is a
result of the far greater sample size. Individual members
can produce local centers of action in their variability
similar to those seen in ERA20C but the magnitude isalways considerably smaller (Fig. 3).
Where stippling is present in Figs. 2a–j the variability issignificantly different from white noise at the 5% (gray)and 1% (white) levels. The lack of stippling in Figs. 2f–j
indicates that the variability within LENS is, for the mostpart, not distinguishable from the sampling of white noise;that is, the variability is entirely consistent with the properties of a time series that exhibits no correlation from one
year to the next and there is, therefore, no need to invoke
an underlying causal mechanism acting on long time scales(Wunsch 1999; Feldstein 2000). Indeed, the variability in
an atmosphere-only control simulation with prescribedclimatological SSTs, where there is unlikely to be muchof a source of persistence from one year to the next
(with the possible exception of memory from the land),is found to be very similar to that in these forced coupled runs (not shown).
In ERA20C in the early winter, over the North Atlantic,
the variability is also not greater than expected fromsampling white noise. There are regions over Europe andNorth Africa for which this is not the case and, while not
our focus, these will be discussed briefly in section 5. Over
the North Atlantic, as the winter progresses, the variability
becomes increasingly outside of the range expected fromwhite noise, becoming significantly different from white
noise at the 1% level in March (Fig. 2e). In fact, the variability in March of the area averaged U700 over the North
Atlantic subpolar gyre (green boxed region in Fig. 1e, referred to as U700NA hereafter) lies around the 99.96th
percentile of white noise samples; that is, there is lessthan a 1 in 2500 chance that the observed variability in
20-yr means has arisen from the sampling of white noiseinterannual variability, or given that four separate months
have been sampled, less than a 1 in 625 chance of obtaining
one month that exhibits this degree of variability. It should
be noted that similar conclusions hold if instead synthetic
time series were generated from the fit to an autoregressive red noise (AR1) process, since the autocorrelationfrom one year to the next is typically only around 0.2 andthe associated added persistence in the synthetic time series does not substantially alter the stippled regions.
The U700NA index was chosen based on the region
that exhibits the greatest multidecadal variability. To
place this index into context, we show regression maps of

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FIG. 3. As in Fig. 2j, but for the individual LENS historicalmember that exhibits the (a) maximum and (b) minimum March
20-yr running mean s in U700NA (green box in Fig. 2e).

March U700 onto the March U700NA index for ERA20C(Fig. 4) and CESM LENS (Fig. 4b) on interannual timescales. Variability in U700 that accompanies this index
is similar between ERA20C and CESM LENS and ischaracterized by zonally symmetric anomalies across the
Atlantic sector, representing a poleward shifting of theAtlantic jet in the western part of the basin and astrengthening at the jet exit region. These zonal wind
anomalies are very similar to those that accompany thedominant (shifting) mode of variability in the Atlantic
jet stream (e.g., Eichelberger and Hartmann 2007, theirFig. 2b). While the structure of the anomalies is very
similar between ERA20C and CESM LENS, due to differences in the climatological jet position, they represent
a strengthening of the jet stream over a wider portion of
the basin in CESM LENS.
That the variability in DJFM averaged U700 is
significantly different from white noise in ERA20C(Fig. 2a) but not in CESM (Fig. 2f) is consistent with
the NAO analysis of Kim et al. (2018). However, while

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FIG. 4. (a) Interannual regression of March U700 onto March U700NA for ERA20C from 1900 to 2010. The
green box shows the area used to define the U700NA region, and black points depict the local climatological jet
maximum at each longitude determined by a quadratic fit to the maximum U700 on the 28 grid and the U700 at thetwo adjacent grid points. (b) As in (a), but for the ensemble mean of regressions calculated from the individual
CESM LENS members. (c) The 20-yr running mean s of U700NA as a function of the number of years in therunning mean for March from 1920–2010. Data are shown for ERA20C (black circles) and the 20CR reanalysis(black asterisks); gray shading indicates the 95% confidence interval (2.5–97.5 percentile value range) on the 20-yr
running mean s for a white noise process with the ERA20C interannual standard deviation, the gray dashed line
indicates the 97.5 percentile values for a white noise process with the ERA20C interannual standard deviation plus
0.75, the blue boxes and whiskers indicate the 2.5–97.5 percentile value range and minimum and maximum foroverlapping 91-yr chunks of the LENS piControl, red dots indicate s for each LENS historical member, and bluelines indicate the 2.5–97.5 percentile value range of a white noise process with the LENS piControl interannual
standard deviation. (d) The s of 20-yr running means of 31-day running means of U700NA for ERA20C from 1920
to 2010 along with the 95% confidence interval for the white noise distribution (gray shading) and the 1st and 99th
percentiles of the white noise distribution (black dotted).

variability in U700NA is strongly correlated with SLPbased NAO indices, these NAO indices do not pick up
the same degree of seasonality in the variability as thisanalysis of U700, nor do they pick up the same degree of
discrepancy between the models and reanalyses since
this U700 variability also depends on the finer-scaledetails in how the local SLP gradients change, not only
the large-scale pressure patterns that characterize theNAO. This is discussed in more detail in appendix A.
The extent to which these conclusions are dependent on
the length of the running mean considered can be assessedfrom Fig. 4c, which shows the standard deviations ofMarch running means of a variety of lengths for U700NA.
First, the CESM piControl and LENS simulations arevery comparable in their behavior, as are ERA20C and
20CR. For anything longer than about an 8-yr runningmean, the reanalysis variability is greater than that seenin any of the 90-yr segments of CESM simulation considered. The interannual standard deviation (value of 1
on the x axis of Fig. 4c) of the reanalyses lies within the
distribution of the estimates for CESM, but the range of
CESM values suggest that the uncertainty on the observed estimate could be as much as 60.75. Even if thetrue interannual standard deviation of the real world
were equal to the observed standard deviation plus 0.75,
beyond about a 10-yr running mean, the variability still
exceeds the expectations from white noise sampling(gray dashed line in Fig. 4c).
The division into calendar months is somewhat arbitrary so the standard deviation of 20-yr running means of
U700NA is shown for 31-day running means throughout
the course of the year for ERA20C in Fig. 4d. This,

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8321

FIG. 6. Maximum standard deviation of 20-yr running means for
March for 1900–2005, considering all grid points in the Atlantic
domain (region plotted in Fig. 2). The black dashed (dotted) line
indicates the maximum value for ERA20C (20CR). Blue points
indicate the maximum value considering all overlapping 106-yr
chunks of the preindustrial control simulations; red points indicate
the maximum value for each historical member. Black circles in the
final two columns indicate maximum values for simulations withprescribed historical SSTs.

FIG. 5. Standard deviation of 20-yr running means of U700NA
for 1900–2005 for (a) DJFM, (b) December, (c) January, (d) February,and (e) March. The blue boxes and whiskers indicate the 2.5–97.5
percentile value range and min and max of overlapping 106-yr chunksof the piControl simulations; red circles indicate historical members, the black dashed lines ERA20C, and black dotted lines 20CR.
Gray shading indicates the 95% confidence interval for 106-yr
chunks of white noise with the ERA20C interannual standard
deviation. Blue dots are similar to gray shading, but use theinterannual standard deviation for the piControl run of eachagain, demonstrates the greatly enhanced multidecadal
variability in the late winter in the North Atlantic,
reaching greater values than expected from white noise
sampling only in late February and March. Since thechances of obtaining variability in 20-yr means as large
as seen in March by sampling a white noise time series
are less than 1 in 2500, then even if we account for themultiple (;12) tests that have been performed here, thechances of obtaining an extreme value like March arestill less than 1 in 200.
So, CESM is very likely different from the reanalyses
in terms of its representation of multidecadal variability in North Atlantic U700, but how do other models
compare? The assessment of CMIP5 U700NA variability shown in Fig. 5 demonstrates that the CMIP5
models also sit within the expectations from white noise
sampling (HadGEM2-ES in March and IPSL-CM5BLR in the DJFM average are the only exceptions). Thisis also true of the CESM and ECMWF AMIP simulations (black points; i.e., those with prescribed historicalindividual model. Black circles in last two columns indicate values
for prescribed SST simulations. Percentages to the right indicate the percentile of the white noise distribution at which
ERA20C lies.

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SSTs). The variability in ERA20C in March is roughly
double the mean value for each model and 50% greaterthan the maximum value sampled taking all 106-yr
segments for each model into consideration.
One remaining possibility is that perhaps the models
are capable of simulating the observed magnitude ofvariability, but they just do not do it in exactly the same
location. This possibility can be ruled out by an assessment of the maximum s of 20-yr means of any grid point
within the Atlantic domain (Fig. 6). For the vast majority of models, the maximum multidecadal variability
seen in ERA20C is around 50% greater than the maximum variability that occurs anywhere in the North
Atlantic sector. Only two models (HadGEM2-ES and
IPSL-CM5A-LR) come close in a 106-yr segment of
their piControl simulation.
To summarize, the multidecadal variability in U700 in
the North Atlantic in CESM and in the vast majority of
CMIP5 models is entirely consistent with the samplingof white noise interannual variability (i.e., there is noneed to invoke a particular low-frequency cause behindthe modeled multidecadal variability). The same is true
for the early winter (December and January) of theobserved record. In contrast, in the late winter (lateFebruary and March) the observed record displays
multidecadal variability that is highly unlikely to haveoccurred as a result of the averaging of year-to-year
variability indicating that possibility 1 outlined in the
introduction for explaining the discrepancy between
models and observations is extremely unlikely to be theonly explanation.

4. The observed multidecadal variability in the
North Atlantic in March
Before proceeding with an assessment of the likelihood of the remaining possibilities (2 and 3), it is worth
briefly considering what this variability actually means
for the climatology of the North Atlantic jet stream
during March. The time series of March U700NA for thevarious different reanalyses are shown in Fig. 7a. First,
even without the application of a running mean smoother,

FIG. 7. (a) Time series of March U700NA (green box in Fig. 2e)
for each reanalysis dataset. (b) March 700-hPa zonal wind for the
31-yr climatology from 1980 to 2010 using ERA20C. (c) As in (b), butfrom 1935 to 1965. (d) PDF of daily jet latitude based on 700-hPa
zonal wind averaged from 608W to 08 and applying a kernelsmoothing to the PDF following Woollings et al. (2010). The PDFs
are for March days after first performing the low-pass filtering sodays prior to and after March are used for filtering. The shading
depicts the 2.5–97.5th percentile value range determined by resampling the individual years within the time period considered,with replacement. The green boxes in (b) and (c) depict the areaused to define the U700NA index for reference.

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the multidecadal variability is clear with a notable
minimum centered on the 1950s, rising to a maximum
in the 1990s, consistent with the positive NAO trends
observed in the wintertime average over this time period. Second, a comparison of the different reanalyses
should allay any concerns over the fidelity of North
Atlantic U700 in reanalyses that are only constrainedby surface pressure observations. ERA20C and 20CR
compare very well with each other over the period of
overlap and they also compare well with the shorter,
more constrained, reanalyses.
The structure of the North Atlantic jet stream at
700 hPa is shown for the 31-yr periods of 1980–2010 and
1935–65 in Figs. 7b and 7c, respectively. This makes
clear that the structure of the observed March North
Atlantic jet stream can vastly differ between 30-yr periods. Notably, the climatological flow over the United
Kingdom in 1980–2010 is roughly double that of 1935–65
with related implications for storminess in that region.
Another metric that is commonly used to characterize
the North Atlantic jet stream is the probability density
function (PDF) of daily jet latitudes. Following a similar
methodology to that of Woollings et al. (2010), this isobtained by first averaging the zonal wind from 08 to
608W, then applying a 10-day low-pass Lanczos filter with
61 weights before identifying the latitude of maximum
zonal wind between 158 and 758N on a daily basis. The
latitude of the maximum is obtained using a quadratic fit
to the grid point with the maximum zonal wind and thetwo adjacent to it. The first three harmonics of the climatological seasonal cycle of jet latitude values are thensubtracted before building the PDF of jet latitudes.
This PDF is shown, centered on the March climatological jet position for the two time periods (1980–2010
and 1935–65) in Fig. 7d. During 1980–2010, the PDF
displays the three preferred jet latitude locations as
discussed by Woollings et al. (2010) for DJF. However,for 1935–65 the PDF is drastically different with a distinct minimum in occupation around 508N and a greatly
increased preference for the occupation of latitudes
between 358 and 458N. Note that this is not at odds withthe conclusions of Woollings et al. (2014), who foundthe trimodal structure of the PDF to be robust over thecourse of the twentieth century as they considered the
DJF season (their Fig. 6). Figure 7d depicts March only
and the DJF season for both periods exhibits the structure described by Woollings et al. (2014) (not shown).
This variability in daily jet latitudes likely also corresponds
to variability in the occurrence of blocking. As shown by
Häkkinen et al. (2011) for the winter season as a whole,
the 1950s and 1960s were characterized by more frequent North Atlantic blocking events compared to thelater time period.

8323

5. The relationship with SSTs and external forcings
The results of section 3 indicate that the chances that
atmospheric noise has given rise to the observed variability in the late winter over the last century are
extremely slim. We therefore proceed to search forevidence of connections with either multidecadal SST
variability or external forcings to identify possible underlying causes of the excess variability.

a. The connection with SSTs
Figure 8a shows the correlation between 20-yr runningmeans of SSTs globally and U700NA during March. HereERA20C from 1900 to 2010 is combined with ERAInterim from 2011 to 2017 and this is correlated withERSSTv5, although results are essentially the same whenusing other combinations of datasets. Strengthened winds
over the North Atlantic on these time scales are highly
correlated with reduced SSTs in the subpolar North Atlantic and increased SSTs over the Southern Ocean. Even
with the very few degrees of freedom that remain when
taking a 20-yr running mean over a 118-yr period, locallythe correlations in the subpolar North Atlantic and inregions of the Southern Ocean exceed the threshold forsignificance at the 5% level.
This global pattern of SST correlations is reminiscentof the SST variability that accompanies the TS AMV
index on these time scales (Fig. 1c). Indeed, U700NA isstrongly negatively correlated with the TS AMV index
during March (Fig. 8b).1 In fact, the correlation between North Atlantic zonal wind and the TS AMV is far
greater in March than in any of the other winter months
or the winter season as a whole (Fig. 8b) and this strong
negative correlation holds if the period considered isextended back to 1854 using 20CR (Fig. 8c). So, themonth of the winter that exhibits excess multidecadal
variability in the North Atlantic winds (i.e., March) is
the month for which the relationship between the AMV
and the winds is by far the strongest.
This strong negative correlation with the TS AMV
index is robust to the SST dataset used (Fig. 8d, left) andthe LIMoptimal AMV index also produces correlationsof a similar magnitude, although they narrowly fail topass the significance thresholds (Fig. 8d, center). In
contrast, when the linearly detrended AMV index isused, the correlation with North Atlantic winds is approximately halved (Fig. 8d, right). Given that the TS
and LIMoptimal AMV indices are designed to isolate

1

The AMV index here is calculated for the individual month orseason, but the correlations are similar if the annual mean AMV
index is used.

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FIG. 8. The relationship between SSTs and U700NA (green box in Fig. 2e). (a) Correlation between SST (ERSSTv5) and U700NA(ERA20C 1 ERA-Interim, ERA-Interim after 2010) and 20-yr running means in March from 1900 to 2017. White stippled regions are
significantly different from zero at the 5% level by a two-sided test although these regions do not pass a stricter test that accounts for spatial
correlation by the false discovery rate method of Wilks (2016). Correlation between U700NA and AMV for 20-yr running means [bluehatched (solid) 5 significant at the 10% (5%) level by a two-sided test]: (b) correlations evaluated over 1900–2017 using the TS AMV
index for ERSSTv5 and ERA20C 1 ERA-Interim (ERA-Interim after 2010); (c) as in (b), but for the full record from 1854 to 2017 using
a combination of 20CR and ERA-Interim for U700NA (ERA-Interim after 2014); and (d) correlations for March evaluated from 1900 to2015 using the ERA20C 1 ERA-Interim combination but for various different definitions of the AMV index and different SST datasets.
(e) Time series of March zonal wind and 21 3 AMV index for 20CR 1 ERA-Interim and the TS AMV index for ERSSTv5. (f) As in(e), but for 20-yr running means. All significance testing has been performed using the resampling strategy described in section 2d. In(b)–(d) the correlation has been performed using the AMV from that individual month or seasons except for the LIMoptimal index
where the JFM averaged AMV has been used.

only the internal component, the multidecadal variability
in March winds is, therefore, most strongly connected tothe internal component of AMV.
Normalized time series of the unfiltered and 20-yr
running mean TS AMV and North Atlantic winds arepresented in Figs. 8e and 8f, extending back to 1854.
There is a clear correspondence between them at low
frequencies.
If we assume, for the moment, that this relationshiprepresents an influence of SST variability on U700NA,
then one possible reason why models fail to reproduce
the degree of variability seen in the real world could be
because they fail to simulate the magnitude of AMV thathas occurred over the observational record, as shown by anumber of studies (Frankcombe et al. 2015; Murphy et al.
2017; Qasmi et al. 2017; Kim et al. 2018). But the CESM
and ECMWF AMIP simulations similarly fail to reproduce the observed magnitude of U700NA variability(Figs. 5e and 6) and, much like the coupled models, arewithin the range expected from white noise sampling.
Therefore, prescribing the historical evolution of SSTs
does not solve the issue. Note that this was also theconclusion drawn by Kim et al. (2018) for NAO variability of the winter season as a whole in CESM.
Figure 9 shows the regression of zonal wind onto the
TS AMV index for the observations; that is, we assume
U700NA 5 bAMV 1 « ,(1)

15 OCTOBER 2018

SIMPSON ET AL.

FIG. 9. (a)–(e) The regression of 20-yr running mean ERA20C
U700 onto the TS ERSSTv5 AMV index (scaled by 0.1, which isapproximately the standard deviation of the AMV in each month).
Colored regions indicate where the regression coefficient is morethan 50% larger than the maximum regression coefficient foundacross the 10 CESM AMIP members. The regions where the regression coefficient is large are also those where the correlation coefficient is high [most regions with a regression coefficient greaterthan 0.5 m s21 (0.1 K)21 have a correlation coefficient greater than0.8]. (f) The regression of 21-yr running mean U700NA onto AMV(scaled by 0.1 K) for each month. The 21-yr running means are usedhere such that the low-frequency time series is centered on a particular year. Error bars show the 2.5–97.5 percentile value range ofbootstrapped estimates of the regression coefficients. These havebeen estimated by first dividing the U700NA and AMV time series
into low-frequency (21-yr running means centered on a given year)
and high-frequency (the actual value for that year minus the lowfrequency time series) components. The high-frequency component
is then resampled with replacement (preserving the relationship between U700NA and AMV) to produce a new high-frequency timeseries of equivalent length to the full record. The 21-yr running meanof this high-frequency time series is added to the low-frequency timeseries and the regression coefficient is recalculated. This is repeated1000 times and the error bars show the 2.5–97.5 percentile value
range of these bootstrapped regression coefficients. Note that (a)–(e)use the time series from 1900 to 2009 for comparison with the CESMAMIP experiments while (f) makes use of 1900–2017, appending
ERA-Interim to ERA20C after 2010.

8325

where ( ) represents a 20-yr mean, and « representsnoise that is unrelated to AMV. Figure 9 shows b,
scaled by 0.1 K, which is approximately the standard
deviation of AMV in each month. Colored regions in
Fig. 9 indicate where the magnitude of b is more than
50% greater than the maximum regression coefficient
found across the 10 CESM AMIP members. In March,
the observed b is more than 50% larger than the
maximum found in the AMIP members over much of
the North Atlantic (Fig. 9d). In fact, there is a striking
correspondence across the winter season between theregions where U700 is strongly related to the AMV
and those where the observations exhibit enhanced
multidecadal variability, both relative to white noise
expectations and relative to CESM. This begins withthe regions over Europe and North Africa in the early
winter (cf. Figs. 9a and 2l) and evolves to the regions
over the North Atlantic Ocean in March, and to alesser extent February (cf. Figs. 9c,d with Figs. 2n,o).
This indicates a strong possibility that these regions
are being influenced by SST variations that accompany
the AMV index and that models (at least CESM andthe ECMWF model) do not sufficiently respond toobserved SSTs.
We can further assess whether the relationship between U700 and AMV is actually different in Marchfrom the other winter months by considering the uncertainty in b. The b values calculated from the regression of U700NA onto AMV (scaled by 0.1 K) are
shown in Fig. 9f along with the 2.5–97.5 percentile value
ranges. These uncertainty ranges are estimated using abootstrapping with replacement methodology as described in the figure caption. The probability that theDecember, January, and February b values are equivalent to the March b is p 5 1.6 3 1025, 6.4 3 1023, and
0.141, respectively, where these p values represent theprobability that the March value of b and the value ofb for the other month fall within the region where theirconfidence intervals overlap. We can, therefore, be
confident that the relationship between AMV and
U700NA is different in December and January fromthat in March, but it is possible that a similar relationshipexists in February and March with sampling of noise
leading to the differences seen between them.

b. The connection with external forcings
SST variability has the potential to give rise to themultidecadal North Atlantic U700 variability in March,but is there also a potential role for external forcings that
could either act directly on the North Atlantic winds orindirectly through their influence on the SSTs? The factthat U700 is only strongly connected with the internallygenerated component of AMV is already suggestive that

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this is not the case, but here we also examine whether
combinations of GHG, aerosol, volcanic, or solar forcings have the potential to be drivers of the U700 variability through a multiple linear regression approach.
The forcing time series used are the global, annual average radiative forcings due to total greenhouse gas
forcing (GHG), total direct aerosol forcing (AER),
volcanic stratospheric aerosol forcing (VOL) and total
solar irradiance (SOL) provided by Otto et al. (2015).
Normalized versions of these time series with zero mean
and unit standard deviation are shown in Fig. 10a.
We assess to what extent a linear regression model of
20-yr running mean U700NA fit to 20-yr running meansof these forcing time series can explain the variability
in March U700NA between 1900 and 2017. If all four
predictors are included and the full record length is used
to fit the regression model, then the U700NA predictedby this model is correlated at 0.94 with the actual
U700NA (red asterisk in the second column of Fig. 10d).
This actually slightly exceeds the correlation with U700NA
predicted by a linear regression model fit to the AMV(0.89; red asterisk in the first column of Fig. 10d). However,the limited degrees of freedom available with 20-yr running means of a 118-yr time series mean that overfittingwith the regression model when multiple predictors areused is very likely.
To avoid this, we take a cross-validation approach byassessing whether the regression model derived by fitting to a portion of the time series can explain the variations seen in the remainder. A demonstration of thisapproach is shown in Fig. 10b for the regression model
fit to all four forcings. In this example, the regression
model is fit only using U700NA between 20-yr means
centered on 1935 and 1975 (red dotted portion) andbelow we summarize the results for similar fits over all
overlapping 40-yr segments. The parameters obtained
from the fit to this portion are then used to predict

FIG. 10. An assessment of the ability of global forcings and theAMV to predict U700 in the North Atlantic. (a) Forcing time series, normalized to have zero mean and unit standard deviation,

taken from Otto et al. (2015). (b) A demonstration of the crossvalidation procedure used to assess the effectiveness of a given
regression model; in this case all forcings in (a) are included as
predictors. Shown are the 20-yr running means of North Atlantic
U700 (black) and the predicted U700 from the regression fit to thedotted red section (green). Insets i and ii depict the anomalies
from the mean within the segments not used to fit the regression
model. (c) As in (b), but using the ERSSTv5 TS AMV as thepredictor. (d) Box and whiskers indicate the 10th–90th percentile
range, min, max, and median of the correlation coefficients obtained from the cross-validation procedure using the ERSSTv5
TS AMV and various forcing combinations (G 5 GHG, A 5 AER,
V 5 VOL, S 5 SOL). Red points show the correlation between
predicted and actual U700 when the full time series are used to fit
the regression model.

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SIMPSON ET AL.

U700NA in the remainder of the time series. To assess
the ability of the regression model to do this, the meanfrom the time series within each segment is first subtracted (insets i and ii of Fig. 10b) to remove any built-in
correlation that would arise from the long term variations over the time period used for the fit. The time series from segments i and ii are then combined, and thePearson correlation coefficient between the predicted
U700NA and actual U700NA for the combination ofsegments i and ii is calculated. In the example shown in
Fig. 10b, the regression fit over the red dotted segment
fails dramatically to predict the variations in the remainder of the time series giving a correlation of 20.32.
In contrast, an equivalent example for the fit to theAMV does a reasonable job, giving a correlation of 0.8(Fig. 10c).
Repeating this procedure for fits over all 59 overlapping segments of 40-yr length gives the 10th/90th
percentiles and min/max of the correlation coefficientsshown in Fig. 10d. The regression model is considered to
fail when the 10th–90th percentile range is below, or
encompasses, zero; that is, when more than 10% of thetime, the predicted U700NA is negatively correlatedwith the actual U700NA. All regression models fit to
combinations of external forcings fail this test. Even the
median correlation typically lies below zero. In contrast,
the regression onto the AMV passes convincingly.2 This
lends additional support to the conclusion that the multidecadal U700NA variability is most strongly related
to the internally generated AMV.

c. Comments on ocean–atmosphere causality
While the above analysis leads us to conclude that themultidecadal variability in March U700 is most strongly
connected with the AMV, some ambiguity remains overthe causal nature of this connection. It is well known thatvariability in the North Atlantic atmospheric circulation
and associated surface fluxes is, itself, a driving force forNorth Atlantic SST variability (Deser and Blackmon
1993; Deser et al. 2010), whether it be through the influence on the deep ocean circulation (Eden and Jung
2001; Danabasoglu et al. 2014; Yeager and Danabasoglu
2014; Delworth and Zeng 2016; Delworth et al. 2017) ordirect influence on the ocean mixed layer (Seager et al.
2000; Clement et al. 2015; Cane et al. 2017). It is,therefore, plausible that these connections indicate a
causal link in the opposite sense (i.e., the multidecadal

2
The forcing regression also fails if the segment length used forthe fit is increased while the AMV regression continues to pass,although the AMV regression does also start to fail if segments
shorter than about 40 years are used.

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variability in the winds is driving the multidecadal variability in the SSTs). While the coupling between ocean
and atmosphere likely goes both ways, we summarize
here various lines of reasoning that support a directedcausal link between the SST variability and March
U700 in the sense of the SSTs driving March U700.
First, if SSTs do not explain the excess March U700
variability, then either an extremely unlikely occurrence by chance or external forcings would have to be
invoked. External forcings are unlikely given that 1) thewinds are most strongly connected to the AMV indicesthat are designed to isolate the internal component and2) the regression analysis in the previous section fails toprovide a link with external forcings.
Our remaining lines of reasoning are arguments forwhy the correlation between AMV and U700 does not
represent a connection in the sense of U700 driving the
AMV and, therefore, more likely represents a connection in the sense of the AMV influencing U700. First,
consider the spatial structure of the SST anomalies that
accompany the March U700 variability. On short timescales, NAO variability gives rise to a tripole pattern of
SST anomalies through the direct influence of alteredsurface fluxes on the ocean mixed layer (Seager et al.
2000; Deser et al. 2010; Delworth et al. 2017). The highfrequency variability in U700NA, which is strongly
connected to the NAO (see appendix A), is similarly
associated with a tripole pattern (Fig. 11a). The nearsurface wind anomalies that accompany a 1 m s21 increase in U700NA are very similar on low and highfrequencies (cf. vectors in Figs. 11a and 11b). Therefore,
if the low-frequency SST variations that accompany
U700NA are a direct result of the influence of the windvariability on the ocean mixed layer, then we should
expect the regression of low-frequency SST onto lowfrequency U700NA to exhibit the same tripolar structure as seen at high frequencies. While this is the case for
CESM (cf. Figs. 11c and 11d), it is not the case for theobservations (cf. Figs. 11a and 11b). In observations, theSST anomalies that accompany the 20-yr running mean
U700NA variability show a distinct pattern with one center in the subpolar gyre and oppositely signed anomalies
elsewhere (except along the southeast United States).
Delworth et al. (2017) recently argued that a structure,similar to this, is a signature of an influence of the ocean
circulation. This would then imply that the March winds
are not driving the multidecadal AMV through their direct
influence on the ocean mixed layer alone.
The possibility remains that the March winds are a key
driver of the accompanying SST anomalies through theirinfluence on ocean heat transport. But, if that were thecase, then there should be a lagged relationship between
the wind variability and the SSTs given the long time

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FIG. 11. The regression of SSTs (shading) and near-surface winds (vectors) onto U700NA (box in Fig. 2e) for March. (a),(b) Regressions
for the observations (SSTs 5 ERSSTv5, winds 5 ERA20C 1 ERA-Interim 10-m winds, 1900–2017); (c),(d) the mean regression for the
LENS historical ensemble (lowest model level winds are used). Note that (a) and (c) show the regressions on high frequencies and (b) and(d) show the regressions on low frequencies. Low frequencies are defined as 21-yr running means centered on a given year while highfrequencies are the actual value minus the low-frequency value for that given year. The zero contour for SST is shown in green. The boxedregion in (b) depicts the area used for the SST time series shown in Fig. 13.

scales inherent to the ocean circulation. Indeed,
Delworth et al. (2017) showed that this SST pattern lags
NAO variability in the observed record by at least 20
years (their Fig. 2h). This brings us to our third line ofreasoning based on lead–lagged correlations between
U700NA and AMV. Previous studies have inferred adriving of the AMV by the NAO through the use oflead–lagged correlations between low-frequency timeseries of the wintertime averaged indices (Peings et al.
2016; Delworth et al. 2017; Kim et al. 2018). Unsurprisingly, a similar lead–lagged relationship occurs
between the wintertime averaged (DJFM) U700NA
and the AMV (black lines in Fig. 12b). A positive AMV
index (i.e., a warmer subpolar North Atlantic) is preceded by a positive anomaly in U700NA about 20–30
years earlier.
However, if there is an instantaneous connection between the AMV and U700NA, then given the strong

autocorrelation of the AMV (Fig. 12a), a lagged correlation between U700NA and the AMV would be expected due to their instantaneous connection alone.
We account for this possibility by removing this built-in
correlation, that is, the lag-zero correlation between
AMV and U700NA multiplied by the lagged autocorrelation of AMV (Fig. 12c), from the cross correlation
between U700 and AMV, giving the red lines in Fig. 12b.
For DJFM this shifts the lag of the maximum correlationslightly but the conclusion remains that a positive AMV
index is preceded by a positive anomaly in U700NA. It
should be noted that this same lead–lagged relationshipis present, although noisier, for 10-yr running means(dotted lines in Fig. 12), and so it is not an artifact of thefilter used (Cane et al. 2017). These correlations are notlarge enough to pass the 10% threshold for significance,
but previous studies (e.g., Eden and Jung 2001;
Delworth et al. 2017) have explicitly shown that the15 OCTOBER 2018

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8329

FIG. 12. The 20-yr running means (solid) and 10-yr running means (dotted). (a) The autocorrelation of the AMV index [note the flippedvertical axis for ease of comparison with (c), (e), and (g)]. (b) The lagged correlation between U700NA and the AMV index for DJFM
(black) and the same but after subtraction of the built-in correlation shown in (c) (red). (c) The built-in correlation for DJFM obtained
from the lag-0 correlation between U700NA and the AMV index multiplied by the AMV autocorrelation. (d),(e) And in (b) and (d), but
for DJF. (f),(g) As in (b) and (d), but for March. In (b)–(g) the gray line shows a reference value from the DJFM panel, 20-yr runningmeans, showing the maximum of the black solid line in (b), (d), and (f) and the minimum of the black solid line in (c), (e), and (g). The
AMV used in all panels is the DJFM average TS index for ERSSTv5, and U700NA is from ERA20C 1 ERAInterim (1900–2017, ERAInterim after 2010).

ocean circulation and the AMV respond to prescribed
NAO surface fluxes with a similar lead–lagged relationship, suggesting they are physically meaningful.
Figures 12d and 12e are equivalent to Figs. 12b and12c but using DJF U700NA while Figs. 12f and 12g use
March U700NA only. These show that the March winds
are not key to producing this lead–lagged relationshipbetween the DJFM U700NA and AMV. In fact, astronger correlation at negative lags is found when only
the DJF winds are considered and, while a lead–lagged
relationship with the March winds is present, it is relatively weak once the built-in correlation from their instantaneous connection is removed. The March winds,
therefore, do not appear to be a critical component of
the wind variability that precedes the AMV. Rather,based on the above reasoning, it is likely that the strong
instantaneous negative correlation between March winds
and the AMV (Fig. 12f) is evidence of an influence of
AMV on the winds and that, while the March winds may
contribute in return to the driving of the AMV, the otherwinter months likely dominate.
Our final, and perhaps most compelling, line of reasoning comes from the behavior of the CESM initialized

decadal predictions. When initialized with observationbased ocean and sea ice conditions, CESM can, to alarge extent, predict the behavior of SSTs in the region
south of Greenland (i.e., the region that exhibits thestrongest correlation with U700NA) a decade ahead(Fig. 13a). This prediction skill has already been discussed with an earlier model version by Yeager et al.
(2012) and for this ensemble by Yeager et al. (2018).
While the full twentieth century cannot be analyzed inthese simulations, it is clear that over the latter half ofthe century, the initialized simulations can capture thelow-frequency evolution of the SSTs south of Greenland, beginning with the decrease toward the late 1980s
and the subsequent increase toward 2010. The freerunning LENS simulations, on the other hand, do not
capture this variability (Fig. 13b), so it not arising fromthe predictive power of the external forcings. Given thattypical decay time scales of SST anomalies in this region
are on the order of two years (Deser et al. 2003), if thewinds are responsible for this low-frequency evolutionof the SSTs, then the low-frequency behavior of thewinds would have to also be predicted by the decadal
prediction ensemble. This is not the case (Fig. 13c). Even

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FIG. 13. (a) Time series of DJFM averaged SST anomalies in the North Atlantic (boxed region in Fig. 11b), black
is ERSSTv5, gray is the prediction from each individual decadal prediction ensemble member that was initialized 10
years earlier, and red is the ensemble mean of the gray lines. (b) As in (a), but the model values are from the freerunning CESM LENS historical simulations. (c),(d) As in (a) and (b), but for U700NA in the North Atlantic box
using ERA20C 1 ERA-Interim (ERA-Interim after 2010) as the observational dataset and using only a 5-yr leadtime for the decadal prediction ensemble. All time series are normalized by the mean over the 1964–2016 period.
For the decadal prediction simulations in (a) [(c)] the values used are the anomalies of that 10th (5th) year prediction from the climatology of all 10th (5th) year predictions.

with a lead time of only 5 years, the decadal prediction
ensemble fails to predict the low-frequency evolution of
the winds (the same is true of longer lead times too). The
predictability of the SSTs, instead, lies in the initialization of the ocean circulation and the associated skill in
the prediction of ocean heat transports. This provides
further evidence that the instantaneous connection between March U700NA and the SSTs south of Greenland
does not represent a direct driving of the SSTs by thewinds and that variations in ocean heat transport are an
integral component of this low-frequency SST variability. Without a need to invoke the March U700 variability
to explain the SST anomalies, we have no reason to
expect them to be so highly correlated unless something
about the SST anomalies is driving variability in U700.

6. Discussion and conclusions
The analysis presented in section 3 indicates that observed multidecadal variability in North Atlantic zonal
wind displays a pronounced seasonality with strongly
enhanced variability in the late winter, specifically late

February and March. Models fail to exhibit this degreeof late winter variability, and they almost all exhibit
variability that is entirely consistent with the samplingof uncorrelated year-to-year fluctuations of Gaussian
white noise. The first of the three possible reasons forthis deficiency laid out in the introduction was that theobserved variability was the random chance samplingof internal year-to-year atmospheric variability, with noneed to invoke an underlying low-frequency cause, andthat either models are deficient in their year-to-year
atmospheric variability or the observed sequence ofvariability was unlikely. While it is possible that the
sampling of atmospheric noise has contributed to thevariability that has been observed, the results demonstratethat the observed and modeled interannual variability arecomparable and that the observed multidecadal variability was exceedingly unlikely to have occurred through thechance sampling of atmospheric noise alone, motivating
the search for an underlying cause.
There are two main categories of low-frequency
forcing that could explain the variability in question.
One is forcing that is external to the ocean–atmosphere15 OCTOBER 2018

SIMPSON ET AL.

system (e.g., anthropogenic forcings) and the other isinternal ocean–atmosphere coupled variability, whereby
the long time scales inherent to the ocean act as a lowfrequency forcing on the atmospheric circulation. It isfound that the variability in March zonal winds isstrongly correlated with a global pattern of SST variability that is characterized by anomalies in the subpolar
North Atlantic with oppositely signed anomalies in theSouthern Ocean. This is the pattern that is represented
by traditional AMV indices on these time scales (Fig. 1c)and indicates that low-frequency variability in the ocean
does have the potential to explain the low-frequency
variability in North Atlantic winds. If so, then given thatsimulations with prescribed historical SSTs were shown
to also be deficient in their North Atlantic jet stream
variability, it suggests that models may be deficient intheir response to the SSTs.
The conventional view of North Atlantic ocean–
atmosphere coupling, however, is that the primary direction of interaction is the opposite of this, with thewinds influencing the SSTs. Evidence for a connection in
the other direction, during winter, is somewhat patchy.
Consistent with the results of the present study, Ting
et al. (2014) and Gastineau and Frankignoul (2015) haveboth shown in reanalyses of the twentieth century that in
the winter seasonal average, the positive phase of AMV
is associated with a negative NAO signal; Gastineau andFrankignoul (2015) further argued based on lead–lagged
relationships that this connection represents a drivingof the NAO by the underlying SST variability. Other
studies have investigated the impact of the decadal timescale AMV (Fig. 1b) on the North Atlantic atmosphericcirculation using models with prescribed SST anomalies(Sutton and Hodson 2007; Hodson et al. 2010; Peingsand Magnusdottir 2014; Omrani et al. 2014; Davini et al.
2015; Peings and Magnusdottir 2016) or, more recently,
with nudging methodologies in a coupled framework(Ruprich-Robert et al. 2017). The multimodel studyof Hodson et al. (2010) found no significant influence on
the North Atlantic atmospheric circulation during winter but the studies since then have generally argued for a
response in the sense of positive AMV being accompanied by a negative NAO. This response is, however,generally smaller than what would be inferred from theobservational record over the central Atlantic [e.g.,compare Peings and Magnusdottir’s (2014) Fig. 2a with
their supplemental Fig. 6]. In addition, the majority ofstudies focus on the winter season as a whole and theabove analysis suggests these connections may havea rather strong seasonality within the winter months.
Peings and Magnusdottir (2014) is one exception, where
they did find a stronger response in the late winter, but
their subsequent analysis with a different model (Peings

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and Magnusdottir 2016) showed the opposite seasonality. If our hypothesis that models may be deficient intheir response to SST variability in late winter is correct,
then it is not surprising that the response found inmodeling studies is generally weak or inconsistent.
However, given the strong evidence for atmosphericforcing of North Atlantic SSTs, one should rightly be cautious of ascribing a causal link in the sense of the SSTs
influencing the North Atlantic winds from their strong correlation alone. We take a number of lines of reasoning toargue that this correlation does indeed represent an influence of the SST variability on the winds as opposed to theother way round. First, it is difficult to explain the windvariability through external forcings as demonstrated by the
regression analysis in section 5b. Admittedly this approach
was limited by making use of only a linear regression modeland only the global forcing time series of greenhouse gases,
aerosols, volcanic stratospheric aerosol, and solar variability.
It is conceivable that there are nonlinearities or additional
external forcings that could play a role, or more regionalvariations in external forcings that might have a different
phasing through the century and these possibilities havenot been considered here. But, a further line of evidence
against a role for external forcings is that the zonal windvariability is most strongly connected with the internallygenerated component of SST variability, as determined by
the LIMoptimal method of Frankignoul et al. (2017), suggesting external forcings do not dominate. This leaves only
an influence of the SSTs or an extremely unlikely chancesampling of atmospheric variability as explanations.
The remaining lines of reasoning argue against the correlation with SSTs representing an influence of the winds
on the SSTs and, therefore, there is no reason to expect
such a correlation to occur unless the SSTs are influencing
the winds. This includes the fact that the structure of the
SST anomalies that accompany the variability is not what
would be expected to result from the direct forcing by
surface fluxes, which is suggestive of a role for heat transport by the deeper ocean circulation in the SST anomaliesof importance. Then, if the March atmospheric variability
were driving changes in the deeper ocean circulation, a
lagged relationship would be expected rather than the instantaneous one that is found. Finally, a driving of the SSTs
by the March winds is extremely difficult to reconcile with
the CESM decadal predictions, which can predict the lowfrequency evolution of SSTs in the region of relevance up
to a decade in advance without predicting the lowfrequency behavior of the winds themselves.
Each of these factors leads us to conjecture that the strong
correlation between U700 and SSTs represents a driving of
U700 by the SSTs and that possibility 3 laid out in the beginning of the paper is likely playing an important role. Thatis, the excess multidecadal variability in observed March

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North Atlantic winds owes its existence to coupled ocean–
atmosphere processes and the fact that models are deficientin their SST-forced wind variability. It is then possible that
the March wind variability feeds back onto the North
Atlantic SSTs, which could go some way to explaining why
modeled AMV typically exhibits a shorter time scale(Peings et al. 2016; Ting et al. 2011) and weaker amplitude(Frankcombe et al. 2015; Murphy et al. 2017; Qasmi
et al. 2017; Kim et al. 2018) than observed.
This argument would be made all the more convincing
if a mechanistic understanding of the SST impacts on thewinds could be obtained. This understanding currently
eludes us given that it cannot be investigated in the current
generation of models and achieving a mechanistic understanding through observations alone requires analysis
dating back to the 1950s to capture a sufficient magnitudeof the variability. This entails observations made before
the satellite era when we may be concerned about the fidelity of aspects that may be important forcings of U700
variability (e.g., the divergent circulation or transient eddy
fluxes), although this is a current topic of investigation.
It also remains to be understood why the seasonality
in the relationship between AMV and U700NA exists.
There are only small differences in the 1900–2017 climatological jet structure between the winter months,
with the jet being slightly stronger and farther poleward
in the early winter compared to the late winter. It is
possible that these small differences somehow make thejet stream more responsive to AMV. Another possibility
is that the AMV is forcing U700 anomalies throughout
the winter but there is some time scale over which theanomalies grow, reaching a maximum in the later winter. There is some indication of the signal being present
in January and growing in the subsequent months from
Fig. 9, although without a more detailed understandingof the mechanisms involved we can only speculate thatthese could be reasons for the seasonality.
It has been argued that North Atlantic SST variability
may influence the North Atlantic jet through a stratospheric pathway (Omrani et al. 2014) and we have investigated this possibility. In appendix B, evidence
against this for an explanation of the March variability
considered here is discussed. Other possible mechanisms for an AMV influence on the jet stream include
forcing from the tropical Atlantic (Davini et al. 2015;
Peings et al. 2016) or the local influence from the North
Atlantic SSTs on, for example, the forcing of stationary
waves or baroclinicity and eddy–mean flow interactions(Kushnir 1994; Msadek et al. 2011; Gastineau andFrankignoul 2012; Peings et al. 2016), or it could evenarise from forcing from more remote regions suchas the tropical Pacific, or changes in the tropical circulation that arise from the altered interhemispheric

VOLUME 31

temperature gradients induced by the global SST pattern that accompanies the AMV (Fig. 1c).
In general, it is thought that the influence of local North
Atlantic SST anomalies on the atmospheric circulation issmall (Kushnir et al. 2002, and references therein). But thisconclusion has mostly been drawn from model-based analyses and, given their apparent deficiencies, it is possible thatthe real world atmosphere exhibits a greater response to
extratropical SST anomalies. Indeed, recent studies havesuggested that improved resolution of ocean fronts andthe overlying atmosphere could significantly alter thenature of ocean–atmosphere coupling (Smirnov et al.
2015; Parfitt et al. 2016; Siqueira and Kirtman 2016). A
coupled simulation with CESM with horizontal resolution of 1/48 in the atmosphere and 1/108 in the ocean
does not show improved multidecadal variability in the
North Atlantic jet stream (not shown) but this simulation also exhibits a greatly reduced amplitude of AMV
compared to observations. It has yet to be seen whether
a high-resolution atmospheric model subject to realistic
AMV SST variability exhibits an atmospheric response
like that inferred from observations.
The coming decade or two of observational data may
prove critical to fully understanding this issue. If the lowfrequency behaviors of March winds and North Atlantic
SSTs continue to evolve in step, our confidence in theirconnection will be reinforced. Furthermore, if the CESM
decadal predictions prove correct (Fig. 13a), over the
coming decade we should continue to see elevated SSTs
in the North Atlantic and, from our analysis, we would
predict that this would be accompanied by a more zonal
jet stream in March with considerably reduced westerlies
in the region west of the United Kingdom (as in Fig. 7c).
This may give us a period of time that is analogous to the
1940s and 1950s but with sufficient data coverage to fully
diagnose the mechanisms behind the SST–March winds
connection in the observational record.
Acknowledgments. The National Center for Atmospheric Research is sponsored by the National Science
Foundation. KAM was supported by the Advanced
Study Program at NCAR, and EAB was supported by
NSF Award AGS-1545675. IRS is grateful to Guillaume
Gastineau for providing the LIM optimal SSTs and to
Richard Seager and Camille Li for helpful commentson the manuscript as well as Who Kim, Steve Yeager,
Gokhan Danabasoglu, Justin Small, Angie Pendergrass,and Kevin Trenberth for helpful discussions. The CESM
large ensemble and decadal prediction projects were
performed with supercomputing resources provided
by NSF/CISL/Yellowstone. We acknowledge the World
Climate Research Programme’s Working Group on
Coupled Modelling, which is responsible for CMIP, and15 OCTOBER 2018

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we thank the climate modeling groups (listed in Table 1 ofthis paper) for producing and making available their model
output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison
provides coordinating support and led development ofsoftware infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thankthree reviewers for their helpful comments.

APPENDIX A
Comparison with SLP-Based NAO Indices
Here we clarify to what extent the U700 variability isconnected with the SLP-based NAO: both the stationbased (ST) and the empirical orthogonal function(EOF)-based indices (Hurrell 1995). The station-based
NAO index is defined as the SLP difference between
Lisbon, Portugal, and Reykjavik, Iceland. The EOFbased NAO index is the principal component (PC) timeseries of the first EOF of deseasonalized monthly SLP
over 208–808N and 2708–408E (cosine weighted). The
EOF is calculated using all months in the DJFM season
and the PC time series is normalized such that it has unit
standard deviation for all months and years combined.
These NAO indices have been a commonly used measure of Atlantic jet stream variability in previous studies(e.g., Kravtsov 2017; Wang et al. 2017; Kim et al. 2018).
The 20-yr running means of the NAO and the
U700NA index used in the main body of the text arehighly correlated during January, February, and March,
but not during December (Fig. A1a). This is because theU700 variability that accompanies the NAO on thesetime scales differs from month to month (not shown, but
can be inferred from the SLP regression in Figs. A1h–k).
It follows that the NAO is also significantly correlatedwith the AMV during March (correlation 5 20.91 for
ST, 20.88 for EOF), but much less so during the otherwinter months (Fig. A1b).
While, during March, our U700NA index is correlatedwith the SLP-based NAO indices, a slightly different
conclusion as to the seasonality of the variability and theextent to which models differ from observations would
be obtained using the SLP-based NAO indices. Fig. A1cshows the s of 20-yr running means of the ST NAO
index for each month of the year, for ERA20C (black
points) and the LENS historical members (red points) as
well as the EOF-based NAO index for ERA20C only in
December, January, February, and March (blue points).
March does not stand out as so unusual when the NAO
indices are considered. In fact, for the EOF-based index,
January and February exhibit more variability. Thatbeing said, it is only during March that the ST NAO
variability exceeds the expectations from white noise
sampling.
Insights into the reason behind this difference can beobtained by considering the variability in 20-yr runningmeans of SLP over the Atlantic domain (Figs. A1d–g).
The variability over Reykjavik and Lisbon (green
points) does not differ too much among January, February, and March, consistent with their similar ST-based
NAO variability (Fig. A1c). However, March differsfrom the other months by having greatly enhanced SLPvariability more localized over the midlatitude North
Atlantic Ocean in a region that would not be picked upby the ST-based NAO index. Similarly, on these low
frequencies the regression of SLP anomalies onto the
EOF-based NAO exhibits different structures acrossthe season (Figs. A1h–k). While the NAO pattern is,by construction, the same for each month, the regressionof SLP onto the NAO index on these 20-yr mean timescales exhibits enhanced SLP anomalies (and associatedU700 anomalies; not shown) in the central Atlantic in
March compared to the other months (Fig. A1k). The
NAO indices, therefore, do not pick up on these detailed features and seasonal variations that are key tothe enhanced U700 variability in March relative to theother months.

APPENDIX B
An Assessment of the Potential Role of aStratospheric Pathway
It has been argued that the forcing of the North Atlantic jet by the AMV and the trends over the latter halfof the twentieth century may have been driven via astratospheric pathway (Scaife et al. 2005; Omrani et al.
2014). Through comparison of the response to a warming of North Atlantic SSTs in high-top and low-top
model versions, Omrani et al. (2014) argued that a
warming of the North Atlantic induces a strengtheningof the polar vortex in midwinter that then propagates
downward leading to a negative NAO anomaly in late
winter. It is, however, difficult to reconcile the magnitude of the observed March zonal wind variability on
20-yr mean time scales with this argument.
While the DJF polar vortex strength of ERA20C
compares reasonably well with more constrained reanalyses (Fig. B1a), one may be skeptical of the fidelity of
ERA20C stratospheric winds further back in time. So,
here we consider the change in the stratospheric polar
vortex between two 20-yr periods that lie within the
JRA-55 record: 1981–2000 and 1958–77. While the8334

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FIG. A1. (a) Correlation between 20-yr running mean U700NA and the EOF-based and ST-based SLP NAO indices for ERA20C 1
ERA-Interim, 1900–2017 (ERA-Interim after 2010). (b) Correlation between the TS ERSSTv5 AMV index and the EOF- and ST-based
NAO indices, 1900–2017. (c) The 20-yr running mean s of the NAO indices from 1920–2010. The left axis relates to the ST index and theright axis relates to the EOF index. Shown are the ERA20C ST index (black points), the ST index for the LENS historical members (redpoints), the 2.5–97.5 percentile value range of white noise samples of the ST index calculated using the ERA20C interannual s (grayshaded region) and the same for the 1st and 99th percentiles (dotted), and the EOF index for ERA20C (blue asterisk). Note that the EOF
points should not be compared with the model and white noise estimates for ST shown in the panel. (d)–(g) The s of 20-yr running meansof SLP from 1920 to 2010 for ERA20C. Gray stippling indicates where the variability is significantly different from white noise at the 5%
level accounting for spatial correlation by the false discovery rate method of Wilks [2016, their Eq. (3)], with a false discovery control valueof 20%. (h)–(k) The regression of 20-yr running mean SLP onto 20-yr running means of the EOF index. Note that differing lengths ofrecord are used for each panel for consistency with comparable figures for U700 in the main text (Figs. 2, 4, and 8), but conclusions are notdependent on the exact record length used.

difference between these periods does not quite span thefull range of variability seen in 20-yr means over the
century, it spans a substantial portion of it with the latertime period being characterized by greatly enhanced
U700 in the North Atlantic compared to the earlier time
period (Fig. B1c).

By the argument of Omrani et al. (2014), if thestratosphere were a key player in driving this response
we should expect to see a strengthening of the polar
vortex in midwinter in the later period, compared to theearlier period. Indeed, such a strengthening is found, but
it is rather small compared to the interannual variability

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FIG. B1. (a) DJF averaged zonal mean zonal wind area averaged from 608 to 808N at 70 hPa for all the reanalysis products. (b) The meandifference in 608–808N zonal mean zonal wind between the late and early periods depicted by the red lines in (a). Late is 1981–2000, andearly is 1958–77. (c) The difference in March U700 between the late and early periods. (d) The composite mean difference in 608–808N
zonal mean zonal wind between strong and weak vortex years. Strong (weak) vortex years are defined as those with DJF 608–808N, 70-hPa
zonal mean zonal wind stronger (weaker) than average over the 1958–2016 period. (e) Composite mean difference in March U700
between strong and weak vortex years.

(Fig. B1b; also, compare the difference between the
red lines in Fig. B1a with the interannual variability).
To assess whether the magnitude of the March U700
anomalies is consistent with driving from the stratosphere, we compare with anomalies related to interannual variability in the polar vortex. Figure B1d shows
the composite mean difference in polar vortex zonal
winds between years when the DJF polar vortex isstronger than average with years when the DJF polar
vortex is weaker than average. Here years have been
composited from the full record length (1957–2016).
The interannual composite displays polar vortex
anomalies at 70 hPa during DJF that are around 6 times
stronger than the difference between the 1981–2000 and
1958–77 periods (cf. Figs. B1b and B1d) yet the March
U700 anomalies in the North Atlantic are around half
the magnitude (cf. Figs. B1c and B1e). In order for thechanges in the stratospheric polar vortex to explain the
difference between these two time periods, an explanation would be needed for why the stratospheric differences between the later and earlier periods are roughly
12 times more effective at producing March troposphericzonal wind anomalies than interannual stratosphericvariability is. It therefore seems unlikely the March zonal
wind anomalies are stratospherically driven.

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