RESEARCH LETTER
10.1029/2018GL081393
Key Points:• Modeled trajectories to, and time of
emergence of, an ice‐free Arctic are
modulated by concomitant shifts in
Paciﬁc Ocean variability• Faster sea‐ice loss during the shift
from the negative to the positive
phase of the Interdecadal Paciﬁc
Oscillation leads to earlier ice‐freeArctic• There is increased likelihood ofaccelerated Arctic sea‐ice loss overthe coming decades if current shift ofInterdecadal Paciﬁc Oscillation
continues
Supporting Information:• Supporting Information S1

Correspondence to:J. A. Screen,j.screen@exeter.ac.uk

Citation:
Screen, J. A., & Deser, C. (2019). Paciﬁc
Ocean variability inﬂuences the time of
emergence of a seasonally ice‐freeArctic Ocean. Geophysical Research
Letters, 46, 2222–2231. https://doi.org/
10.1029/2018GL081393
Received 21 NOV 2018
Accepted 4 FEB 2019
Accepted article online 5 FEB 2019
Published online 15 FEB 2019

Paciﬁc Ocean Variability Inﬂuences the Timeof Emergence of a Seasonally
Ice‐Free Arctic Ocean
J. A. Screen1

and C. Deser2

1

Global Systems Institute and Department of Mathematics, University of Exeter, Exeter, Devon, UK, 2Climate and Global
Dynamics, National Center for Atmospheric Research, Boulder, CO, USA

Abstract The Arctic Ocean is projected to become seasonally ice‐free before midcentury unlessgreenhouse gas emissions are rapidly reduced, but exactly when this could occur depends considerably oninternal climate variability. Here we show that trajectories to an ice‐free Arctic are modulated byconcomitant shifts in the Interdecadal Paciﬁc Oscillation (IPO). Trajectories starting in the negative IPO
phase become ice‐free 7 years sooner than those starting in the positive IPO phase. Trajectories starting in
the negative IPO phase subsequently transition toward the positive IPO phase, on average, with anassociated strengthening of the Aleutian Low, increased poleward energy transport, and faster sea‐ice loss.
The observed IPO began to transition away from its negative phase in the past few years. If this shiftcontinues, our results suggest increased likelihood of accelerated sea‐ice loss over the coming decades, andan increased risk of an ice‐free Arctic within the next 20–30 years.
Plain Language Summary Manmade climate change is causing a rapid loss of Arctic sea ice.
Summer Arctic sea ice is predicted to disappear almost completely by the middle of this century, unlessemissions of greenhouse gases are rapidly reduced. The speed of sea‐ice loss is not constant over time,
however. Natural climate variability can add to the manmade decline, leading to faster sea‐ice loss, or can
subtract from the manmade decline, leading to slower sea‐ice loss. In this study, we looked at how natural
climate variability affects the timing of an ice‐free Arctic. We found that a natural cycle called theInterdecadal Paciﬁc Oscillation, or IPO for short, is particularly important. Arctic sea‐ice loss is faster whenthe IPO is moving from its cold to warm phase and slower when the IPO is moving from its warm tocold phase. This is because variations in the IPO cause changes in atmospheric wind patterns, which alter
the amount of heat that is transported into the Arctic. Observations show that the IPO started to shift
from its cold to warm phase in the past few years. If this shift continues, our results suggest that there is anincreased chance of accelerated sea‐ice loss over the coming decades.

1. Introduction
Arctic sea‐ice cover has declined in recent decades across all calendar months (Kay et al., 2011; Stroeve &Notz, 2018). This decline directly follows the rise in global mean surface temperature (Mahlstein &Knutti, 2012; Niederdrenk & Notz, 2018; Rosenblum and Eisenmann, 2016, 2017) and anthropogenic CO2
emissions (Notz & Stroeve, 2016). The Arctic will become seasonally ice‐free before midcentury unlessgreenhouse gas emissions are rapidly reduced (Notz & Stroeve, 2018; Stroeve et al., 2012) and even thestrictest emissions reduction targets may be insufﬁcient to prevent occasional ice‐free summers (Jahn,
2018; Screen, 2018; Screen & Williamson, 2017; Sigmond et al., 2018). An ice‐free summer could conceivably
occur within the next two decades (Jahn, 2018).

©2019. American Geophysical Union.
All Rights Reserved.

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Superimposed on the long‐term Arctic sea‐ice decline is the year‐to‐year and decade‐to‐decade variability
due to internal climate variability (Day et al., 2012; Kay et al., 2011; Swart et al., 2015; Zhang, 2015).
Internal variability may be responsible for 40–50% of Arctic sea‐ice decline observed over the past 37 years(Ding et al., 2019). Looking to the future, internal variability exerts a strong inﬂuence on the timing of theﬁrst ice‐free summer (Jahn, 2018; Jahn et al., 2016). For a given greenhouse gas emissions scenario, thistiming can vary by 20 years due to internal variability alone (Jahn et al., 2016). This means that it is possible,
although less likely, for an ice‐free Arctic to occur earlier in a low emissions scenario than a high emissions
scenario (Jahn, 2018). Despite recognition of the important role of internal variability in the timing of an2222

Geophysical Research Letters10.1029/2018GL081393

ice‐free Arctic, the physical mechanisms responsible have not been explored in much detail. There is
growing evidence that tropical decadal variability can inﬂuence sea‐ice trends in both hemispheres (Dinget al., 2017, 2019; Meehl, Arblaster, et al., 2016; Meehl et al., 2018; Purich et al., 2016; Schneider & Deser,2017). Wettstein and Deser (2014) showed that internal variability is an important contributor to near‐term
projections of Arctic sea‐ice extent. Higher rates of summer ice loss were found to be related to large‐scaleatmospheric circulation anomalies, including a Rossby wave train from the tropical Paciﬁc. However, to
our knowledge, our study is the ﬁrst to speciﬁcally consider the role of tropical decadal variability inmodulating the time of emergence of a seasonally ice‐free Arctic.

2. Data and Methods
In this study, our primary tool for exploring Arctic sea‐ice variability is the Community Earth System Modelversion 1 (CESM1) Large Ensemble (CESM1‐LE; Kay et al., 2015). The CESM1‐LE consists of 40 parallel
simulations covering the years 1920 to 2100. Each individual simulation, referred to hereafter as an ensemble member, is performed with the same model version and with the same external forcing (e.g., greenhousegas concentrations, ozone, aerosols, and land use). External forcing follows observed values from 1920 to2005 and then the Representative Concentration Pathway 8.5 (RCP8.5) from 2006 to 2100. Representative
Concentration Pathway 8.5 is a high‐end emissions scenario, which in this model leads to global‐averagesurface warming of 2 °C, relative to preindustrial, in approximately year 2040 and ~5 °C warming by 2100
(Kay et al., 2015). Ensemble members only differ from each other in their initial atmospheric conditions(through a round‐off level perturbation to atmospheric temperature), which ensures that each member
has a unique time sequence of internal (unforced) climate variability. More details can be found in Kay
et al. (2015). We also make use of an 1800‐year CESM1 control run with constant year 1850 external forcing.
To document the observed changes, we use the satellite record of Arctic sea‐ice extent provided by the
National Snow and Ice Data Center (Fetterer et al., 2017) for the period 1979–2017 and version 5 of the
NOAA Extended Reconstructed Sea Surface Temperature data set for the period 1880–2017 (Huang
et al., 2017).
We use the common deﬁnitions for Arctic sea‐ice extent and for an ice‐free Arctic, which are the total area ofgrid cells with a sea‐ice concentration of at least 15% and sea‐ice extent below 1 × 106 km2, respectively. The
Interdecadal Paciﬁc Oscillation (IPO) index is deﬁned from 11‐year running mean annual‐mean SST using
the tripole index of Henley et al. (2015). Before calculating the IPO index, we removed the externally forced
response to retain only the internal variability. For CESM1‐LE, we subtracted the ensemble‐mean SST fromeach ensemble member, for each year and at each grid point. For the NOAA Extended Reconstructed Sea
Surface Temperature, we removed an estimate of the forced response deﬁned as the 137‐year (1880–2017)linear trend at each grid point. The Aleutian Low (AL) index is deﬁned by the area‐averaged mean sea levelpressure over North Paciﬁc (160–220°E, 30–65°N), consistent with the North Paciﬁc Index of Trenberth andHurrell (1994). Here we have reversed the sign of the AL index such that positive values correspond to astrengthened AL (i.e., reduced mean sea level pressure). The IPO and AL indices were normalized by removing their mean and dividing by their standard deviation. Unless otherwise stated, all analyses were conducted using 11‐year running means.

3. Results
3.1. Trajectories to an Ice‐Free Arctic
Figure 1a shows observed September sea‐ice extent (SSIE) from 1980 to 2017 and simulated SSIE from 1980
to 2060, the latter from the CESM1‐LE. Since an ice‐free Arctic will emerge ﬁrst in September (Jahn, 2018), if
at all, we focus solely on this month. The ensemble‐mean depicts the emergence of ice‐free conditions in
2046. Note that this is when the 11‐year running mean SSIE ﬁrst falls below 1 × 106 km2, which provides
an indication of when frequent ice‐free Septembers emerge; occasional ice‐free Septembers are simulated
sooner than this. Note that the terminology “time of emergence” is often used to describe when anthropogenic climate change becomes separable from internal climate variability (Hawkins & Sutton, 2012); however, we use this wording to simply denote the time when the 11‐year running mean SSIE ﬁrst falls
below 1 × 106 km2.
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Figure 1. Arctic sea‐ice projections. (a) September sea‐ice extent (SSIE) in observations and the Community Earth System
Model version 1 Large Ensemble (CESM1‐LE). The observed sea‐ice extent is shown with a thin black curve and its 11‐year
running mean with a thick black curve. The simulated ensemble‐mean 11‐year running mean is shown by the blue
curve and the 2σ (95%) range by gray shading. Horizontal lines denote the observed present‐day (2007–2017 average) SSIE
62
6
2
(4.7 × 10 km ) and the common threshold for an ice‐free Arctic (1 × 10 km ). Orange crosses and circles denote the62
62
year when each ensemble member is closest to 4.7 × 10 km and the year when they ﬁrst fall below 1 × 10 km ,
respectively, and their size denotes the number of members meeting these criteria in a single year. (b) As (a), but the for
SSIE trajectories. (c) SSIE trajectories, categorized by the Interdecadal Paciﬁc Oscillation (IPO) index in year 0. Thinblue, gray, and orange lines denote trajectories starting in negative IPO (< −0.9σ), neutral IPO (> −0.9σ and <0.9σ) andpositive IPO (>0.9σ), respectively. The thicker orange and blue lines denote the average of the positive and negative
IPO cases, respectively, and have a black border where they are statistically different from each other (p < 0.05). Filled
circles show when the average trajectories ﬁrst become ice‐free. (d) As (c), but for the IPO trajectories. Note that thex axis in (b)–(d) is the trajectory year and not the calendar year.

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In this study we are primarily concerned with the rapidity of the transition from the present‐day sea‐ice state(deﬁned as the 11‐year period 2007–2017) to ice‐free conditions. Figure 1a (speciﬁcally, the crosses) showsthat some ensemble members reach the present‐day observed SSIE sooner than other ensemble members.
To account for this, in each ensemble member we identiﬁed the year when the 11‐year running‐mean
SSIE most closely matches the observed SSIE for the period 2007–2017; we then use this year as the starting
point (denoted year 0) for our forward trajectory analysis. This procedure yielded 40 SSIE trajectories, which
all start from approximately the same value and are unbiased relative to recent observations. The same starting points were used to calculate corresponding trajectories for other variables. These starting points (crosses
in Figure 1a) are later than 2007–2017, which is a reﬂection of the overestimation of SSIE in CESM1‐LE.
The ensemble‐mean SSIE trajectory, which represents the best estimate of the externally forced response,
reaches ice‐free conditions in year 27 (Figure 1b). The onset date of an ice‐free Arctic ranges from year 17
to year 37 across the ensemble members. This 20‐year range due to internal variability is consistent withlarge ensembles from other climate models (Jahn et al., 2016). Looking backward in time, the ensemble‐
mean SSIE trajectory captures well the observed sea‐ice evolution. Thus, by considering simulated trajectories, as supposed to the raw model output, we circumvent the issue of a small SSIE bias (relative to many
CMIP5 models, see e.g., Stroeve et al., 2012) in CESM1‐LE (Figure 1a).
Figure 1b suggests that according to this model and emissions scenario, ice‐free Septembers could occur
within two decades from now. Previous studies have suggested that regional sea‐ice cover may be predictableon this timescale (Germe et al., 2014; Guemas et al., 2016; Koenigk et al., 2012; Yeager et al., 2015; Yang et al.,2016). Here we investigate whether the current phase of slowly evolving ocean variability could shift theodds in favor of an earlier or later emergence of ice‐free conditions. We search for evidence of this in
CESM1‐LE by correlating the time of emergence of an ice‐free Arctic in each trajectory against the 11‐year
running‐mean SST in the ﬁrst year (hereafter termed year 0) of each trajectory (Figure 2a). This is an application of the so‐called ensemble correlation (Wettstein & Deser, 2014), which seeks to explain how ensemble
diversity in one variable is statistically related to ensemble spread in another variable. Figure 2a is constructed to show the SST pattern in year 0 that is associated with an earlier occurrence of ice‐free conditions(i.e., the sign of the correlation is reversed). It reveals a signiﬁcant relationship between the timing of an ice‐
free Arctic and Paciﬁc Ocean SSTs. More speciﬁcally, an earlier emergence of ice‐free conditions is linked to
an SST anomaly pattern that bears strong resemblance to the negative phase of the IPO (supporting information Figure S1). The ensemble correlations (Figure 2a) imply that SSIE trajectories starting during the negative IPO phase become ice‐free sooner than those starting during the positive IPO phase. Indeed, the IPO
index in year 0 is a good predictor (r = 0.52; p < 0.001) of the timing of an ice‐free Arctic (Figure 2b). We ﬁnd
no clear association between the timing of an ice‐free Arctic and Atlantic Ocean SST in year 0 (Figure 2a).
3.2. Inﬂuence of the IPO
The IPO is a long‐term oscillation of SST in the Paciﬁc Ocean that can last from 10 to 30 years (Henley et al.,2015). During the negative phase, cool SST anomalies occur along the North Paciﬁc coast of North America
and warm SST anomalies in the interior North Paciﬁc (supporting information Figure S1). This trend
reverses during the positive IPO phase. The IPO has a similar surface ocean signature to the Paciﬁc
Decadal Oscillation (e.g., Figure 1a in Newman et al., 2016), but the IPO‐related SST anomalies extend
further into the South Paciﬁc. Our choice to focus hereafter on the IPO, as opposed to other metrics ofPaciﬁc Ocean variability, reﬂects the strong hemispheric symmetry in Figure 2a and the high patterncorrelation between Figure 2a and supporting information Figure S1b (r = 0.9 over the Indo‐Paciﬁc sector,
40°E–100°W, 70°N–70°S). The simulated IPO in CESM1 has a realistic spatial pattern and phase duration(supporting information Figure S1).
The inﬂuence of the IPO on the time of emergence of ice‐free conditions is further shown in Figure 1c. Here
we have split the SSIE trajectories into three categories (positive, neutral, and negative) depending on the
IPO index in year 0. On average, SSIE trajectories starting in the negative IPO phase become ice‐free 7 years
sooner than those starting in the positive IPO phase (year 23 vs. year 30). Also, the trajectories that reach ice‐
free conditions earliest and latest start in the negative and positive IPO phase, respectively. By deﬁnition, theSSIE trajectories lie close to each other in year 0 and remain so for around 5 years but begin to diverge thereafter. The IPO positive and IPO negative average trajectories become statistically separable by year 17 and
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Figure 2. Internal climate variability linked to faster sea‐ice loss. (a) Ensemble correlation between the time of emergence of an ice‐free Arctic and annual‐meansea surface temperatures (SST) in year 0 of each trajectory. Black hatching shows statistical signiﬁcance at the 95% conﬁdence level (p < 0.05). The sign of thecorrelations has been reversed to display the pattern linked to an earlier emergence of ice‐free conditions. (b) Relationship between the timing of ice‐free conditions
and the Interdecadal Paciﬁc Oscillation (IPO) index in year 0. There are 40 circles, one per trajectory, which are colored according the IPO index in year 0(as in Figure 1). The black line denotes the linear best ﬁt, and the colored crosses show averages for the three IPO categories Ice‐free timing (IFT). (c) As (a), but for
the ensemble correlation between the IPO index in year 0 and the annual‐mean SST trend in each trajectory. (d) As (b), but for the relationship between the
IPO index in year 0 and the IPO trend. (e) As (c), but for the correlation between the IPO index in year 0 and the cold season (October–March) mean sea levelpressure (MSLP) trend. (f) As (d), but for the relationship between the IPO index in year 0 and the cold season Aleutian Low (AL) trend. (g) As (c), but for
the relationship between the IPO index in year 0 and the 500‐hPa geopotential height (Z500) trend. (h) Ensemble correlation between the IPO index in year 0 andthe geopotential height trend zonally averaged over the Paciﬁc sector (ZPac). In (a, c, f, g), the sign of the correlations has been reversed to display the patterns linked
to negative IPO in year 0.

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remain distinct until year 40, by which time they have all become ice‐free. Recall that the ensemble‐mean
trajectory, which is our best estimate of the forced response, ﬁrst reaches an ice‐free state in year 27.
Figure 1d shows corresponding trajectories for the IPO index, again categorized by the IPO index in year 0.
By deﬁnition, the IPO positive and IPO negative average trajectories start from opposite IPO phases, andthey remain signiﬁcantly different from each other for over a decade. The two cases display large and opposite tendencies in the IPO in years 5–15. Note that we focus on the IPO tendency, rather than trajectory‐
mean IPO, because we are concerned with the forward evolution of SSIE from the baseline state in year 0.
Trajectories starting from the negative IPO phase transition toward the positive IPO phase, on average,
and trajectories starting from the positive IPO phase transition toward the negative IPO phase. It is during
this period of rapid and opposite IPO change that the SSIE trajectories for IPO positive and negative cases
begin to diverge. Trajectories transitioning from negative toward positive IPO show accelerated SSIE reduction compared to those transitioning from positive to negative IPO.
3.3. Physical mechanisms
To understand why the year‐0 IPO index is a good predictor of the timing of an ice‐free Arctic, we now
explore associations between the year‐0 IPO and subsequent trends in relevant variables, calculated overthe period from year 0 until an ice‐free Arctic is reached in each trajectory (i.e., for ensemble member 1,
we take the linear trend from year 0 to year 30, when the 11‐year running mean SSIE ﬁrst falls below
1 × 106 km2). Figure 2c shows that a negative IPO index in year 0 tends to be followed by SST trends that
resemble the positive IPO phase. This is further borne out in Figure 2d, which shows that the year‐0 IPO
is a strong predictor (r = −0.58; p < 0.001) of the subsequent IPO trend. This can be understood in termsof the IPO trajectories (Figure 1d). More speciﬁcally, because of the oscillatory character of the IPO, negative
IPO (associated with a colder Arctic) in year 0 is typically followed by a switch toward the positive (warmer
Arctic) phase over the subsequent decade or two, and vice versa.
The year‐0 IPO is also associated with subsequent trends in the cold season (October–March) atmosphericcirculation. We focus on the cold season because this is when tropical‐to‐polar teleconnections are mostactive (e.g., Lee et al., 2009; Thomson & Vallis, 2018a, 2018b). Negative year‐0 IPO is linked to subsequent
decreasing mean sea level pressure trends in the North Paciﬁc (Figure 2e), reﬂecting a strengthening of
the AL. Indeed, the year‐0 IPO is a good predictor (r = −0.52; p < 0.001) of the subsequent trend in the
AL intensity (Figure 2f). This can be understood in terms of a transitioning IPO, with a shift toward the positive IPO phase coinciding with a strengthening of the AL. A strengthening AL is expected to increase lower
atmospheric heat and moisture transport into the Paciﬁc sector of the Arctic (Svendson et al., 2018; Tokinaga
et al., 2017). Negative year‐0 IPO is related to geopotential height trends that depict a planetary wave train
from the tropical Paciﬁc to the Arctic (Figures 2g and 2h). Positive height trends in the polar stratosphere
are indicative of a weakening polar stratospheric vortex (Figure 2h). We ﬁnd that connections between
the year‐0 IPO and midtropospheric circulation are broadly similar in the warm season (April–September)and in the cold season over lower latitudes but that the year‐0 IPO is more closely related to circulation
trends over the Arctic Ocean in the cold season (Supplementary Figure 2).
Locally within the Arctic, the year‐0 IPO is strongly correlated with subsequent positive trends in cold season
downward longwave radiation (DLR; Figure 3a) and near‐surface air temperature (Figure 3b). In wintertime, Arctic surface temperatures are largely controlled by the DLR (Lee et al., 2017), and the DLR is to alarge degree driven by horizontal moisture transport convergence (Gong et al., 2017). We therefore interpretthe DLR and Arctic warming trends in Figure 3b to be partially driven by the IPO‐related atmospheric circulation changes (Figure 2). In regions of sea‐ice loss, the DLR will be enhanced by increased ocean‐to‐atmosphere heat and moisture ﬂuxes (Screen & Simmonds, 2010). Warming, or more speciﬁcally fewer
freezing degree days (Stroeve et al., 2018), may reduce sea‐ice growth and contribute to reductions in sea‐
ice thickness across the Arctic (Figure 3c) and sea‐ice concentration near the sea‐ice edge (Figure 3d).
Although thin ice grows faster than thick ice (Stroeve et al., 2018), this negative feedback is projected to
weaken (Petty et al., 2018), implying a strengthening role of atmospheric controls on winter sea ice growth.
Sea ice thinning and retreat of the sea‐ice edge provide the preconditions for reduced SSIE. The cold season
Arctic‐mean sea‐ice thickness reduction is very highly correlated (r = 0.86; p < 0.001) with SSIE loss acrossthe ensemble (Figure 3e). Simulated sea‐ice growth and melt rates provide further evidence of IPO‐related
preconditioning. Winter growth rates and summer melt rates are lower in trajectories starting in the
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Figure 3. Interdecadal Paciﬁc Oscillation (IPO) phase transition affects the Arctic. (a) Ensemble correlation between the
IPO index in year 0 and the cold season (October–March) downward longwave radiation (DLR) trend in each trajectory.
Black hatching shows statistical signiﬁcance at the 95% conﬁdence level (p < 0.05). (b–d) As (a), but for the correlation
between the IPO index in year 0 and the cold season (b) near‐surface air temperature (TAS) trend, (c) sea‐ice thickness(SIT) trend, and (d) sea‐ice concentration (SIC) trend. In (a–d), the sign of the correlations has been reversed todisplay patterns linked to negative IPO in year 0. (e) Relationship between the cold season Arctic‐mean sea‐ice thickness
trend and September sea‐ice extent (SSIE) trend in each trajectory. The black line denotes the linear best ﬁt. (f) Monthly
and Arctic mean sea ice growth rates (sum of congelation, frazil ice growth, and snow‐ice formation), averaged overtrajectories starting in negative IPO (< −0.9σ; blue) and positive IPO (>0.9σ; orange). (g) As (f), but for sea‐ice melt rates(sum of top, bottom, and lateral melt).

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negative IPO compared to those starting in the positive IPO (Figures 3f and 3g). Thus, the faster SSIE decline
in the former is caused by IPO‐related reductions in sea‐ice growth rather than enhanced summer melting.
Winter preconditioning has been previously shown to be an important driver of interannual ﬂuctuations ofSSIE (e.g., Park et al., 2018; Rigor et al., 2002; Williams et al., 2016) and has contributed to recent SSIE trends.
Recent extreme SSIE minima have been linked to thinner winter ice cover that is more vulnerable to summer melt, driven in part by internal atmospheric variability (predominantly positive Arctic Oscillation) in
the late 1980s through mid‐1990s (Lindsay & Zhang, 2005; Rigor & Wallace, 2004). Holland and Stroeve(2011) showed that the variance of SSIE explained by winter sea‐ice precursors, such as winter ice thickness,increases during the transition to a seasonally ice‐covered Arctic. We propose that IPO‐induced preconditioning is a proximal cause for an earlier occurrence of an ice‐free Arctic in trajectories starting in the negative IPO phase than those starting in the positive IPO phase. However, it is likely that other processes also
play a role, for example, tropically induced high‐latitude circulation changes in summer, as suggested byother studies (Ding et al., 2019; Meehl et al., 2018).

4. Discussion
Our study is not the ﬁrst to suggest a speciﬁc role for Paciﬁc Ocean variability in modulating projections of
21st century Arctic sea‐ice loss. Wettstein and Deser (2014) examined a large ensemble of the Community
Climate System Model version 3 (a predecessor to CESM1) and showed that tropical Paciﬁc Ocean variability
modulated SSIE trends over the period 2020–2059. Higher rates of projected SSIE loss were found to be associated with an atmospheric Rossby wave train over the Paciﬁc, which closely resembles that found to be connected to the IPO in this study (cf. their Figures 11 and 12 and our Figure 3).
Meehl et al. (2018) suggest a role for Paciﬁc decadal variability in the accelerated Arctic winter sea‐ice loss
from 2000 to 2014. Interestingly, these authors conclude that reduced convection over the tropical Paciﬁc,associated with the negatively trending IPO from 2000 to 2014, contributed to enhanced winter Arctic sea‐
ice loss. On the face of it, this result seems at odds with our ﬁnding that the transition from negative to positive IPO favors enhanced winter sea‐ice loss. A likely explanation for this apparent discrepancy is that wintersea‐ice loss from 2000 to 2014 occurred primarily in the Barents Sea, whereas future winter sea‐ice loss is
projected to be more widespread. The impact of the IPO on sea‐ice varies by geographic location. Indeed,
Figures 3c and 3d suggests that the transition from negative to positive IPO slows sea‐ice loss in the
Barents Sea. Therefore, our results do not contradict a contributing role of the negatively trending IPO in
the accelerated winter sea‐ice loss from 2000 to 2014 in the Barents Sea. They suggest, however, that in
the future, IPO‐associated sea‐ice changes in the Paciﬁc sector may dominate over opposite‐signed sea‐icechanges in the Atlantic sector.
Paciﬁc Ocean variability appears to have played a key role in the accelerated Arctic warming during the earlytwentieth century (Svendson et al., 2018; Tokinaga et al., 2017). Svendson et al. (2018) concluded that during
the early twentieth century, the Paciﬁc Decadal Oscillation transitioned to a positive phase with a concomitant deepening of the Aleutian Low, which warmed the Arctic by poleward low‐level advection of extratropical air, which is highly consistent with our results.

5. Conclusions
We have examined the inﬂuence of internal climate variability on the trajectory to, and timing of, a seasonally ice‐free Arctic Ocean. In the CESM1‐LE, the speed of transition from present‐day September ice extent
to an ice‐free state is modulated by concomitant changes in the IPO, which are partially predictable from the
IPO phase at the start of the trajectory. More speciﬁcally, trajectories starting in the negative IPO phase arecharacterized by a subsequent positive IPO trend, strengthening of the Aleutian Low, increased poleward
heat and moisture transport, and an earlier emergence of an ice‐free Arctic. Conversely, trajectories startingin the positive IPO phase are characterized by a subsequent negative IPO trend, weakening of the AleutianLow, reduced poleward heat and moisture transport, and a later emergence of an ice‐free Arctic. Trajectories
starting in the positive IPO phase become ice‐free 7 years sooner than those starting in the negative IPO
phase, on average. This 7‐year difference equates to one third of the total uncertainty due to internal variability in the time of emergence of an ice‐free Arctic in the CESM1‐LE. We are cognisant that our results are
based on simulations from a single model and only one possible emissions scenario and thus must be
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interpreted with a degree of caution. It remains for future work to determine whether other models and scenarios exhibit similar behavior.
Our results suggest that the current IPO state may provide predictive information on the likelihood of faster, or slower, sea‐ice loss over the coming decade(s). The observed IPO began to transition away from its
peak negative phase in the past few years (Figure 1d). If the IPO continues to transition toward its positive phase, as is predicted by decadal IPO forecasts (Meehl, Hu, et al., 2016), our results suggest that there
is increased likelihood of accelerated loss of sea‐ice over the coming decade or two, compared to if the
IPO was trending downward, and an increased risk of witnessing an ice‐free Arctic within the next 20–
30 years. We note, of course, that the IPO is not the only factor that will modulate the rate of Arctic
sea‐ice loss in coming decades. Near‐future changes in sea‐ice will also depend on external forcing,including the rate of anthropogenic emissions of greenhouse gases (Jahn, 2018) and aerosols (Gagne
et al., 2015).
Acknowledgments
The authors thank Ben Henley andthree anonymous reviewers for their
feedback. James Screen received
funding from the Natural Environment
Research Council (NE/N018486/1) andthe Leverhulme Trust (PLP‐2015‐215).
The National Science Foundation
supports NCAR. Data are freely
available at the following repositories:
CESM1 Large Ensemble, http://www.
cesm.ucar.edu/projects/community‐
projects/LENS/data‐sets.html; NOAAERSST version 5, https://www1.ncdc.
noaa.gov/pub/data/cmb/ersst/v5/
netcdf/; and NSIDC Sea Ice Index, ftp://sidads.colorado.edu/DATASETS/NOAA/G02135/.

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