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The Weakening of the Stratospheric Polar Vortex and the Subsequent Surface
Impacts as Consequences to Arctic Sea Ice Loss
YU-CHIAO LIANG ,a,b,c YOUNG-OH KWON,b CLAUDE FRANKIGNOUL,b,d GUILLAUME GASTINEAU,dKAREN L. SMITH,e LORENZO M. POLVANI,c,f,g LANTAO SUN,h YANNICK PEINGS,i CLARA DESER,j RUONAN ZHANG,kl
AND JAMES SCREENa

Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwanb
Woods Hole Oceanographic Institution, Woods Hole, MassachusettscLamont-Doherty Earth Observatory, Columbia University, Palisades, New Yorkd
Sorbonne Université, CNRS/IRD/MNHN, LOCEAN/IPSL, Paris, FranceeDepartment of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, CanadafDepartment of Earth and Environmental Sciences, Columbia University, New York, New Yorkg
Department of Applied Physics and Applied Mathematics, Columbia University, New York, New Yorkh
Department of Atmospheric Science, Colorado State University, Fort Collins, Coloradoi
Department of Earth System Science, University of California, Irvine, CaliforniajNational Center for Atmospheric Research Climate and Global Dynamics, Boulder, ColoradokDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, ChinalCollege of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom(Manuscript received 4 March 2023, in ﬁnal form 6 October 2023, accepted 18 October 2023)
ABSTRACT: This study investigates the stratospheric response to Arctic sea ice loss and subsequent near-surface impacts
by analyzing 200-member coupled experiments using the Whole Atmosphere Community Climate Model version 6(WACCM6) with preindustrial, present-day, and future sea ice conditions speciﬁed following the protocol of the Polar
Ampliﬁcation Model Intercomparison Project. The stratospheric polar vortex weakens signiﬁcantly in response to the prescribed sea ice loss, with a larger response to greater ice loss (i.e., future minus preindustrial) than to smaller ice loss (i.e.,
future minus present-day). Following the weakening of the stratospheric circulation in early boreal winter, the coupledstratosphere–troposphere response to ice loss strengthens in late winter and early spring, projecting onto a negative North
Atlantic Oscillation–like pattern in the lower troposphere. To investigate whether the stratospheric response to sea ice loss
and subsequent surface impacts depend on the background oceanic state, ensemble members are initialized by a combination of varying phases of Atlantic multidecadal variability (AMV) and interdecadal Paciﬁc variability (IPV). Different
AMV and IPV states combined, indeed, can modulate the stratosphere–troposphere responses to sea ice loss, particularlyin the North Atlantic sector. Similar experiments with another climate model show that, although strong sea ice forcing
also leads to tighter stratosphere–troposphere coupling than weak sea ice forcing, the timing of the response differs fromthat in WACCM6. Our ﬁndings suggest that Arctic sea ice loss can affect the stratospheric circulation and subsequent tropospheric variability on seasonal time scales, but modulation by the background oceanic state and model dependence need
to be taken into account.
SIGNIFICANCE STATEMENT: This study uses new-generation climate models to better understand the impacts ofArctic sea ice loss on the surface climate in the midlatitudes, including North America, Europe, and Siberia. We focus
on the stratosphere–troposphere pathway, which involves the weakening of stratospheric winds and its downward coupling into the troposphere. Our results show that Arctic sea ice loss can affect the surface climate in the midlatitudes
via the stratosphere–troposphere pathway, and highlight the modulations from background mean oceanic states as well
as model dependence.
KEYWORDS: Stratospheric circulation; Ice loss/growth; Ensembles

1. Introduction

Denotes content that is immediately available upon publication as open access.
Supplemental information related to this paper is available
at the Journals Online website: https://doi.org/10.1175/JCLI-D-230128.s1.
Corresponding author: Yu-Chiao Liang, yuchiaoliang@ntu.edu.tw

The rapid decline of Arctic sea ice, observed by routine satellite observations (Stroeve and Notz 2018), is a dominantconsequence of anthropogenic climate change in the past four
decades (Fetterer et al. 2017). Not only has Arctic sea ice extent decreased substantially (Comiso et al. 2008; Cavalieri and
Parkinson 2012), but sea ice thickness has also reduced (Rothrock
et al. 1999; Serreze and Stroeve 2015; Lindsay and Schweiger
2015; Kwok 2018). The seasonal evolution of Arctic sea ice has
also undergone dramatic changes (Markus et al. 2009; Stroeve
et al. 2014). Under future greenhouse gas emission scenario

DOI: 10.1175/JCLI-D-23-0128.1
Ó 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding
reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
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simulations conducted by state-of-the-art global climate models
participating in phase 6 of the Coupled Model Intercomparison
Project (CMIP6; Eyring et al. 2016), the Arctic Ocean is projected
to become nearly ice-free in boreal autumn by the middle of thetwenty-ﬁrst century (Notz et al. 2020). Highlighted in the Special
Report on the Ocean and Cryosphere in a Changing Climate(IPCC 2019) and the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 2021), the retreat of
Arctic sea ice has exerted profound impacts on local weather, ecosystems, and socioeconomic conditions (Grebmeier 2012; Jung
et al. 2016) and has instigated rigorous debates on whether or notit has contributed to extreme weather events in North America
and Eurasia (e.g., Barnes 2013; Cohen et al. 2014; Mori et al.
2014; Barnes and Screen 2015; Overland et al. 2015, 2016;
Coumou et al. 2018; Blackport et al. 2019; Mori et al. 2019a,b;
Screen and Blackport 2019a,b; Blackport and Screen 2020a,b;
Cohen et al. 2020; Zappa et al. 2021; Smith et al. 2022).
Associated with the rapid loss of Arctic sea ice, a fasterwarming rate in the Arctic than in the tropics or the rest of
the globe (Holland and Bitz 2003; Pithan and Mauritsen 2014;
Goosse et al. 2018; Previdi et al. 2021; Taylor et al. 2021) isobserved (Serreze and Francis 2006; Serreze et al. 2009;
England et al. 2021; Chylek et al. 2022; Rantanen et al. 2022),
recorded in paleoclimate proxies (Park et al. 2019), and simulated by several generations of climate models (Manabe andWetherald 1975; Holland and Bitz 2003; Hahn et al. 2021;
Chylek et al. 2022; Liang et al. 2020, 2021b; Rantanen et al.
2022; Wu et al. 2023; Zhou et al. 2023). This phenomenon, referred to as Arctic ampliﬁcation (AA), implies a reduced
north–south temperature gradient in the lower troposphere,
which has the potential to dynamically link AA and the associated sea ice loss to changes in the jet stream, which in turnmay modulate the amplitude and persistence of troposphericplanetary waves, leading to more frequent extreme events inthe midlatitudes (Francis and Vavrus 2012, 2015; Cohen et al.
2014, 2018; Francis et al. 2018; Zou et al. 2021). Previous studies also proposed that ampliﬁed Arctic warming and sea ice
loss give rise to a nearly barotropic response of the large-scale
tropospheric circulation resembling the Northern Annular
Mode (NAM; Thompson and Wallace 2000) at the hemispheric scale or the North Atlantic Oscillation (NAO; Hurrell1995) over the North Atlantic sector (e.g., Deser et al. 2004;
Singarayer et al. 2006; Seierstad and Bader 2009; Strey et al.
2010; Cassano et al. 2014; Screen et al. 2014; Wettstein andDeser 2014; Deser et al. 2016; Siew et al. 2020; Liang et al.
2021a). These suggested links to midlatitude atmospheric circulation changes and extremes have been referred to as thetropospheric pathway.
Another proposed mechanism linking AA and the midlatitude
circulation builds upon the process of stratosphere–troposphere
coupling (Baldwin and Dunkerton 2001) and is often called
the stratospheric pathway (e.g., Jaiser et al. 2013; Cohen et al.
2014; Kim et al. 2014; Jaiser et al. 2016; Nakamura et al. 2016;
Ruggieri et al. 2016; Xu et al. 2021, 2023). Distinct from thetropospheric pathway, Arctic sea ice loss along with the associated diabatic heating change (and possibly its gradients) ﬁrst
excites waves propagating upward into the stratosphere (Jaiser
et al. 2013; Peings and Magnusdottir 2014a; Kim et al. 2014;

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Sun et al. 2015; Nakamura et al. 2016; Wu and Smith 2016;
Zhang et al. 2018; Liang et al. 2021a; Sun et al. 2022), which
weakens the stratospheric polar vortex via wave–mean ﬂow interactions (Andrews et al. 1987). The perturbed stratosphericanomalies then migrate downward to the near surface with aprojection onto the negative NAM/NAO pattern (Kidston
et al. 2015; Polvani et al. 2017; Domeisen and Butler 2020;
Domeisen et al. 2020; Baldwin et al. 2021; Liang et al. 2021b),
although other studies showed that the impacts of stratosphere–
troposphere coupling associated with sea ice loss may notalways extend to the surface (e.g., Xu et al. 2023). Despite
being dynamically feasible and statistically identiﬁable, sucha chain of processes involves stratospheric and troposphericcirculations at multiple spatiotemporal scales in the presence of large internal variability, leading to small signal-tonoise ratios (Sun et al. 2015; Seviour 2017; Blackport andScreen 2019; Peings 2019; Liang et al. 2020; Sun et al. 2022).
In addition, the short observational record may not provide
robust statistical relationships between Arctic sea ice loss
and stratospheric circulation changes. Thus, one needs to interpret causality with caution.
In an attempt to address the causality issue, a causal network
technique has been applied to observational datasets to verify
that stratosphere–troposphere coupling can indeed link Arctic
sea ice loss, particularly that in the Barents–Kara Seas, to tropospheric circulation changes in midlatitudes (Kretschmer et al.
2016). An alternative, and more common, approach is to conduct numerical experiments using a general or global climate
model (GCM) to explicitly single out the role of stratosphere–
troposphere coupling and subsequent impacts on near-surface
climate under speciﬁc Arctic sea ice loss conditions (e.g., Peingsand Magnusdottir 2014a; Kim et al. 2014; Sun et al. 2015; Zhang
et al. 2018; Sun et al. 2022). However, the GCM experiments
conducted with different models show diverse stratospheric circulation responses to Arctic sea ice loss, with some modelingstudies showing a weakened polar vortex (Kim et al. 2014;
Nakamura et al. 2016) and others an intensiﬁed vortex (Cai et al.
2012; Scinocca et al. 2009; Screen et al. 2013; Sun et al. 2014;
England et al. 2018) or negligible response (Dai and Song 2020;
Peings et al. 2021; Smith et al. 2022; Sun et al. 2022). Such awide range of sensitivity limits the utility of GCMs to improve
our process-based understanding of the stratospheric responses
to sea ice loss and the subsequent tropospheric climate impacts
that are suggested by observations and reanalysis datasets. This
diversity might be linked to the various strengths and spatial
patterns of the sea ice forcing prescribed in the GCM experiments (Screen et al. 2018; Smith et al. 2019), the different background states (Chen et al. 2016; Smith et al. 2017; Labe et al.
2019) and strengths of eddy–mean ﬂow interaction (Smith et al.
2022), and conﬂation of the sea ice–forced response with internal variability (e.g., Seviour 2017; Peings et al. 2021; Sun et al.
2022).
The Polar Ampliﬁcation Model Intercomparison Project(PAMIP) of CMIP6 provides multiple GCM experimentswith identical sea ice concentration (SIC) and sea surface
temperature (SST) conditions, as well as large ensemble sizes,
to investigate the causes and consequences of polar ampliﬁcation (Smith et al. 2019). PAMIP, therefore, offers a useful test

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bed for assessing Arctic–midlatitude linkages resulting fromthe response to sea ice loss via both the stratospheric and tropospheric pathways (e.g., Ronalds et al. 2020; Audette et al.
2021; Peings et al. 2021; Strefﬁng et al. 2021; Smith et al. 2022;
Sun et al. 2022). The ensemble size for the short (14 month)time-slice experiments was recommended to be 100 by the
PAMIP protocol (see Table 1 of Smith et al. 2019), but several recent studies have indicated that more than 100 members are needed to sufﬁciently separate the sea ice–forced
signals from internal atmospheric circulation variability (e.g.,Labe et al. 2019; Liang et al. 2020; Peings et al. 2021; Sun et al.
2022). Moreover, the PAMIP protocol speciﬁed the samebackground mean SST states derived from the coupled modelexperiments of phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Smith et al. 2019), overlooking the effects of different oceanic states on the atmospheric circulation
responses to sea ice loss.
Previous studies have shown that the stratospheric circulation, its coupling to the troposphere, and the tropospheric circulation can be affected by SST anomalies associated with
Atlantic multidecadal variability (AMV; Knight et al. 2005;
Zhang et al. 2019), interdecadal Paciﬁc variability (IPV;
Henley et al. 2015), and their combination (Nishii et al. 2010;
Woo et al. 2015; Omrani et al. 2014; Peings and Magnusdottir
2014b; Kren et al. 2016; Elsbury et al. 2019; Suo et al. 2022a,b;Omrani et al. 2022). Other studies also highlighted that the
Arctic sea ice and the associated temperature responses canbe modulated by AMV (e.g., Osborne et al. 2017; Li et al.
2018; Chen et al. 2021; Luo et al. 2022) or Paciﬁc decadal variability (e.g., Screen and Francis 2016; Svendsen et al. 2018;
Simon et al. 2022; Luo et al. 2022; Suo et al. 2022a,b). Hence,the concurrent phase shifts of AMV and IPV in the earlytwentieth century that occurred in conjunction with rapid
Arctic warming (Tokinaga et al. 2017) suggest combined effects on Arctic climate change. Taken together, it would bealso reasonable to constrain the SSTs to certain phases of
AMV and IPV in the PAMIP experiments to investigate theirmodulating inﬂuence on the circulation response to Arctic sea
ice loss via the stratospheric pathway.
In this study, we use the Whole Atmosphere Community Climate Model version 6 (WACCM6), which has a high-top conﬁguration to better resolve stratospheric processes (Gettelman
et al. 2019), to conduct the PAMIP time-slice atmosphere–land–ocean–sea ice coupled experiments. We carry out suchfully coupled simulations instead of atmosphere-only experiments because some previous studies pointed out the importance of oceanic feedbacks in the atmospheric response to sea
ice loss (e.g., Deser et al. 2016; Screen and Blackport 2019a;
Screen et al. 2018). Unlike these previous studies, our focus ison the stratospheric response to Arctic sea ice loss and the subsequent impacts on near-surface climate. To address the issuesof internal variability and background SST state dependence,both of which render the novelty of this study, we have increased the PAMIP protocol ensemble size to 200 members
and incorporated combinations of different AMV and IPV SST
states in the initial ocean conditions. The model conﬁgurationand experimental details are described in section 2. Section 3
presents the results according to the dynamical framework

stratosphere–troposphere coupling, starting with troposphericprecursors including upward wave activity, followed by thestratospheric circulation response and, ﬁnally, the subsequent
downward impacts on near-surface sea level pressure, temperature, and precipitation. We then analyze the modulating
effects of combined AMV and IPV phases. Results from another GCM (IPSL-CM6A-LR; Boucher et al. 2020) are also
examined to illustrate the model dependence issue. Discussion
is provided in section 4, followed by a summary of results in
section 5.

2. Data and methods
a. Model descriptions
In this study, we use WACCM6 of the Community Earth
System Model version 2 (hereinafter CESM2-WACCM6;
Danabasoglu et al. 2020) developed by the National Center
for Atmospheric Research (NCAR). The WACCM6 adopts
the ﬁnite-volume dynamical core (Lin and Rood 1997) with a
0.98 latitude 3 1.258 longitude horizontal resolution and is thehigh-top version of the atmospheric component model of
CESM2, which has 70 vertical levels from the surface tomodel top at 6 3 1026 hPa (;140 km), in contrast to its lowtop counterpart, the Community Atmosphere Model version 6(CAM6; Danabasoglu et al. 2020), which has only 32 verticallevels with the model top at 2.26 hPa (;40 km). The
WACCM6 has all the same physical parameterizations as
CAM6, except the parameterization scheme of gravity waves(Gettelman et al. 2019): the orographical gravity wave dragparameterizations in WACCM6 and CAM6 are identical, but
the parameters of frontal and convective parameterization in
WACCM6 were changed to better characterize frontogenesis
and the quasi-biennial oscillation (QBO). The WACCM6
has been shown to not only better simulate the stratosphericprocesses and circulation variability than CAM6, including
sudden stratospheric warming events and the self-generated
QBO (Gettelman et al. 2019; Ayarzagüena et al. 2020), butalso better represent the tropospheric circulation comparedto observations or reanalysis datasets, including storm tracks,
stationary waves, and blocking in midlatitudes (Simpson et al.
2020).
To conduct the fully coupled PAMIP time-slice experiments, the WACCM6 is coupled to other model componentsof CESM2: the ocean component is the Parallel Ocean Program version 2 (POP2; Smith et al. 2010), the sea ice component is CICE version 5.1.2 (CICE5; Hunke et al. 2015), theland-ice component is the Community Ice Sheet Model Version 2.1 (CISM2.1; Lipscomb et al. 2019), the land component
is the Community Land Model Version 5 (CLM5; Lawrence
et al. 2019), and the river transport component is the Modelfor Scale Adaptive River Transport (MOSART; Li et al.
2013). More details on the fully coupled conﬁguration can befound in Danabasoglu et al. (2020). We use the speciﬁed
chemistry version of WACCM6, which does not resolve theinteractive chemical contributions (Smith et al. 2014).
The second GCM used in this study is developed by L’Institut Pierre-Simon Laplace (IPSL) Climate Modeling Centre:

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FIG. 1. March SICs averaged over 200 members for (a) preindustrial, (b) present, and (c) future time-slice coupled experiments and(e)–(g) the corresponding September SICs. (d) The evolution of sea ice area for each experiment during the integration period from
April to May of the following year. (h) As in (d), but for turbulent heat ﬂux from ocean to atmosphere averaged over the Arctic domain(608–908N). In (d) and (h), the thick lines represent the ensemble-mean values, while the color shadings delineate the two standard deviation ranges. In (d), the dashed lines are the target SIC values for the nudging.

the IPSL-CM6A-LR (Boucher et al. 2020). The atmosphericcomponent is the LMDZ6A-LR (Hourdin et al. 2020) with a
1.38 latitude 3 2.58 longitude horizontal resolution; it is also
regarded as a high-top model as it has 79 vertical levels spanning from the surface to 0.01 hPa (;80 km). The land component is ORCHIDEE (Krinner et al. 2005). The LMDZ6-LR is
coupled to the Nucleus for European Models of the Ocean
version 3.6 (Madec et al. 2017), which includes the LIM3 sea
ice component (Rousset et al. 2015; Vancoppenolle et al.
2009). More details about IPSL-CM6A-LR can be found in
Boucher et al. (2020).

b. PAMIP time-slice experiments
We conduct the time-slice experiments with CESM2WACCM6 following the PAMIP protocol [see experiments
2.1, 2.2, and 2.3 in Table 1 of Smith et al. (2019)]. The temporal
integration period is from 1 April to 31 May of the following
year, providing 14-month simulations. The CMIP6 radiative
forcing is ﬁxed at its year 2000 level during the integration period. We constrain the sea ice condition in each experiment by
nudging both SIC and sea ice volume (SIV) toward the target
preindustrial, present-day (hereinafter present), and future
SIC and SIV conditions in the Northern Hemisphere (Smithet al. 2019; Peings et al. 2021), while the SIC and SIV in
the Southern Hemisphere are constrained to be identical atpresent-day levels among the three experiments. The nudging
constrains the sea ice thickness to be about 2 m in the

Northern Hemisphere and 1 m in the Southern Hemisphere,
as required by the PAMIP protocol. The SIV, therefore, is derived from the corresponding sea ice area multiplied by the
thickness.
The present SIC conditions were obtained from the 1979–
2008 climatology using the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003). The
preindustrial SIC values were generated from the 31 historicalsimulations of CMIP5 when the global mean temperature isclose to 13.678C, an estimation of the observational global mean
temperature over the preindustrial period (Haustein et al.
2017). The future SIC conditions were obtained from the corresponding CMIP5 projections based on the 30-yr periods when
the global mean temperature exceeds the preindustrial counterpart by 28C under the representative concentration pathway
8.5 emissions scenarios (Hausfather 2020). The parallel sets ofexperiments carried out by IPSL-CM6A-LR use a similar nudging method, but it is implemented to only reach the target SIC,
while the SIV is not directly constrained. The details and performance were documented in Simon et al. (2021).
The spatial distributions of March and September Arctic
SICs in the time-slice runs are presented in Fig. 1. The future
SIC is much smaller than the preindustrial and present SICs
across the Arctic Ocean in September (Figs. 1e–g), whilesmaller future SICs appear mostly in the marginal ice zones,
including the Sea of Okhotsk, Bering Sea, and Labrador Sea,
in March (Figs. 1a–c). Examination of the Arctic sea ice area

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FIG. 2. The distribution of IPV and AMV phases during 1985–2014 from 3 CESM2-WACCM6(circles) and 11 CESM2-CAM6 (triangles) historical simulations. The ﬁlled symbols are thosesamples used in the ICs of our experiments. See text for details.

(SIA) conﬁrms that our nudging technique successfully constrains the simulated SICs (solid lines in Fig. 1d) toward thetarget (SIA; dashed lines in Fig. 1d) throughout the 14-monthintegration period. The smaller SIC and larger area of open
water in the future runs enhance the upward surface turbulent
heat ﬂuxes averaged over the Arctic domain (608–908N) compared to the present and preindustrial runs, especially in autumn and winter when they reach their peak amplitude(Fig. 1h). This seasonality of turbulent heat ﬂuxes is also seenin previous studies (e.g., Deser et al. 2020b, 2015; Sun et al.
2015; Sun et al. 2022). Hence, larger sea ice retreat in our
simulations indeed promotes larger turbulent heat ﬂuxes into
the atmosphere as early as autumn, a factor that might initiate
the subsequent stratospheric and tropospheric responses.
In the following analyses, we refer to the variable (or response)of interest under “strong” sea ice forcing as that from the future runs minus the preindustrial runs, and under “weak” sea
ice forcing as that in the future runs minus the present runs.
Thus, the control or reference run is the future run. We also
consider as “weak” sea ice forcing the present runs minus thepreindustrial runs, and obtain similar results for stratosphericcirculation responses (not shown). The corresponding ﬁgures
for IPSL-CM6A-LR were documented in Simon et al. (2022,
see their Fig. 1).

c. IPV and AMV initial conditions
To test if the atmospheric responses to Arctic sea ice loss
are inﬂuenced by different phases of AMV and IPV, wechoose the ocean initial conditions (ICs) with different AMV
and IPV phases from existing CESM2 simulations and plot
the oceanic state based on AMV indices against IPV indices(Fig. 2). We select 16 years with AMV2 and IPV1 phases(hereinafter AMV2/IPV1) and 14 years for AMV1 andIPV2 (hereinafter AMV1/IPV2) from 3 CESM2-WACCM6
and 11 CESM2-CAM6 historical simulations, which wereconducted following the CMIP6 protocol from 1850 to 2014
(Danabasoglu et al. 2020), using the 1985–2014 period (ﬁlled
solid symbols in Fig. 2). Other combinations of AMV and
IPV states (i.e., AMV1/IPV1 and AMV2/IPV2) could not
be sufﬁciently sampled with the available historical simulations (Fig. 2). The SST variability in the CESM2-CAM6
and CESM2-WACCM6 historical simulations is comparable(Fig. S5 in the online supplemental material), justifying thatICs are sampled from both models. The AMV state is determined by a normalized AMV index, deﬁned as the annual
mean SST averaged over 08–608N (North Atlantic domain)
minus the annual global-mean SST, smoothed with a 10-yr
low-pass ﬁlter [based on the index calculated in Kerr (2000)
and in Enﬁeld et al. (2001) with slight modiﬁcation in the chosen domain and window length for low-pass ﬁlter]. If the
AMV index is larger (smaller) than 0.8 (20.8), we select it as
an AMV1 (AMV2) state. For the IPV, we follow Henley
et al. (2015) to calculate the tripolar SST index and determine
an IPV1 (IPV2) state when the normalized index is larger(smaller) than 0.8 (20.8). We choose 0.8 at the threshold forthese indices in order to increase the sample size.
The ocean initial state for the full depth is sampled on
1 April for each of the 16 AMV2/IPV1 and 12 AMV1/
IPV2 simulations described above. The ICs for land and atmosphere are obtained from present-day mean states of
CESM2-WACCM6 historical simulations. In addition, we
perturb in each case the initial atmospheric temperature ﬁelds

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with small values (the so-called pertlim method; Kay et al.
2015; Deser et al. 2020) to construct a total of 100 ICs for
AMV1/IPV2 and AMV2/IPV1 cases. This approach provides the ICs to initiate our 200-member sea ice loss experiments. We veriﬁed that the initial SSTs in our experiments
persist into winter with consistent AMV and IPV SST patterns (see Figs. S1–S4). A similar procedure is performed insampling ICs for IPSL-CM6A-LR.

d. Random resampling procedure
Recent studies based on PAMIP experiments found thatsampling ﬂuctuations due to internal variability are large (e.g.,Peings et al. 2021; Smith et al. 2022; Sun et al. 2022). For example, Sun et al. (2022) showed that even the sign of thestratospheric circulation response to Arctic sea ice loss can be
opposite between the ﬁrst and the second halves of a 200member ensemble (see Fig. 3a of Sun et al. 2022). Hence, theuncertainty due to internal variability must be robustly estimated. A commonly used method is the random sampling(bootstrapping) procedure (Mudelsee 2010; Pedregosa et al.
2011), which constructs “resamples” from ensemble members(e.g., Oehrlein et al. 2019; Liang et al. 2022a; Sun et al. 2022).
In this study, we randomly sample 200 members from themerged sample of preindustrial, present, and future runs for
10 000 times with replacement. To obtain the distributions of
10 000 ensemble means for the stratospheric responses understrong and weak sea ice forcings, we take their difference andaverage them over the 200 resampled members. Results are
similar if we change the resampled size to 100.

e. Field significanceThe statistical signiﬁcance of the difference maps may beoverestimated if one simply uses the standard two-tailed Student’s t test at each grid point (i.e., local signiﬁcance; Livezey
and Chen 1983; Wilks 2016). To address this issue, Wilks(2016) introduced the concept of “ﬁeld signiﬁcance,” whichconstrains the false discovery rate (FDR). Recent PAMIP studies have used the ﬁeld signiﬁcance to determine the robustnessof sea ice loss–forced circulation changes (e.g., Peings et al.
2021; Sun et al. 2022). We choose the test level aFDR 5 0.1 toachieve a global test level aFDR 5 0.05 for a spatial decorrelation scale of ;1.54 3 103 km (see Fig. 4 in Wilks 2016), as derived from the spatial autocorrelation of geopotential heightﬁeld at 500 hPa (Polyak 1996). We also apply the ﬁeld signiﬁcance to the height–time domain with aFDR 5 0.1 (D. S. Wilks
2019, personal communication).

3. Resultsa. Sea ice loss enhances stratosphere–troposphere
coupling
We begin by examining the time evolution of the stratospheric and tropospheric responses to Arctic sea ice loss.
Figures 3a and 3b show the time evolution of the polar cap–
averaged (658–908N) geopotential height (hereinafter Z) responses from 1000 to 1 hPa, averaged over the 200 ensemble
members for strong and weak sea ice forcings, respectively,

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from 1 September to the following 1 April. Clearly, the strong
sea ice forcing gives rise to stronger negative polar cap Z responses in both the stratosphere and troposphere in autumn
and winter, indicating that a more pronounced stratosphere–
troposphere coupling is established by greater sea ice loss.
Under strong sea ice forcing, the tropospheric precursors
emerge in October and begin to migrate upward into thestratosphere in the middle of November (Fig. 3a). In contrast,
the upward branch is absent or not statistically signiﬁcant inlate November and December under weak sea ice forcing,showing a decoupling between the troposphere and stratosphere in autumn and early winter (Fig. 3b).
Since the stratospheric polar vortex can be perturbed byvertical wave activity ﬂuxes from the troposphere (Polvani
and Waugh 2004), we next examine the Eliassen–Palm (EP)ﬂuxes (Edmon et al. 1980), which manifest the vertical andmeridional propagation of Rossby waves in the vertical–
latitude coordinate, in response to sea ice forcings in November
and December (Fig. 4). Stronger and enhanced upward EP
ﬂuxes are instigated in the lower troposphere and propagate
vertically into the lower stratosphere (;50 hPa) between 608
and 808N in late November when the sea ice forcing is strong(Fig. 4a). After entering the stratosphere, the EP ﬂuxes are refracted equatorward. As the convergence (divergence) of theEP ﬂuxes is a useful measure to diagnose the resultant zonalmean zonal wind deceleration (acceleration) via the wave–
mean ﬂow interaction (Andrews et al. 1987), we superimpose
the zonal-mean zonal wind responses in Fig. 4. We ﬁnd reduced
winds in the lower stratosphere (50–10 hPa) between 608 and808N and in the higher stratosphere (10–1 hPa) centered around408N in the strong ice forcing case (Fig. 4a), consistent with
the EP ﬂux convergence. The dipolar zonal-wind feature in thehigh stratosphere (10–1 hPa) is also largely explained by thewind tendency induced by the divergence and convergence ofEP ﬂuxes. In contrast, fewer upward EP ﬂuxes enter the
stratosphere in late November when the sea ice forcing is
weak (Fig. 4e), and they have no ﬁeld signiﬁcant inﬂuence on
the zonal-mean zonal wind and do not lead to reduced stratospheric circulation. In early and mid-December, the EP ﬂux
and zonal wind responses remain largely similar to those inlate November, but they become somewhat weaker understrong SIC forcing (Figs. 4b,c). In late December, enhanced
EP ﬂuxes instigated in the midtroposphere give rise to a reduced stratospheric circulation with wider extent (Fig. 4d).
For weak SIC forcing, more EP ﬂuxes enter the stratosphere
and weaken the zonal winds there only after mid-December(Figs. 4g,h). These results clearly illustrate that the stronger
sea ice forcing more effectively inﬂuences the stratospheric circulation in November and December via vertical wave propagation and eddy–mean ﬂow interaction than the weaker sea
ice forcing.
Focusing on the stratospheric circulation responses in
December, when they attain maximum strength (recall Figs. 3a,b),
we further analyze the polar cap Z at 10 hPa (hereinafter Z10;
yellow dashed lines in Figs. 3a,b) under the strong and weaksea ice forcings. Here, we aim to estimate how the stratospheric response varies with the strength of sea ice forcing
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FIG. 3. Evolution of polar cap–averaged geopotential height (Z) response from 1 Sep to 1 Apr of the following year
under (a) strong sea ice forcing and (b) weak sea ice forcing. The polar cap is deﬁned as the region covering 658–908N.
In (a) and (b), the black contours denote the ﬁeld signiﬁcance, while the cyan contours show the 5% local signiﬁcance.
The yellow dashed lines highlight the 10-hPa level in December. (c) Resampled distributions of December geopotential height response at 10 hPa (Z10) under strong (red distribution) and weak (green distribution) sea ice forcings.
The dashed, dotted, and solid vertical lines are the ensemble-mean Z10 responses under strong sea ice forcing basedon members 1–100, 51–150, and 101–200, respectively. The percentage of 10 000 resampled 200-member averages
above zero is given in the legend. (d) Resampled ensemble-mean December Z10 responses (solid line) and the twostandard deviation envelope (color shading) as a function of ensemble size; see text for details.

distributions of resampled ensemble means (section 2d). The
strong sea ice forcing results in a Z10 increase by 74 6 61 m(mean 6 two standard deviations), corresponding to a decelerated polar vortex, whereas the weak sea ice forcing only enhances Z10 by 37 6 62 m. Thus, the stratospheric response is
twice as large in the strong sea ice forcing case than the weak
one, while the uncertainty ranges due to internal variability
are comparable. About 99% of the resamples under strong sea
ice forcing are larger than zero (the red distribution), as compared to only 88% under weak sea ice forcing (the green
distribution).
To further estimate the effect of internal variability, we
compare two standard deviations of the resampled Z10 distributions (hereinafter 2xSD), containing about 95% of the resamples, and obtain 61 and 62 m for the strong and weak sea
ice forcing runs, respectively. The similar values indicate thatthe effect of internal variability is insensitive to the strengthof sea ice forcing. Comparison with the ensemble-mean Z10
responses in the strong and weak sea ice forcing cases, which

are 74 and 37 m, respectively, leads to signal-to-noise ratios of
1.18 and 0.66, respectively, reﬂecting that the forced stratospheric responses are relatively small compared to the internal variability, as documented in previous studies (e.g., Lianget al. 2021b; Smith et al. 2022; Sun et al. 2022). Another perspective is to analyze the amplitude of internal variability as afunction of the ensemble size. We are particularly interested
in how many ensemble members are needed for the lower
bound of the 2xSD envelope to be larger than zero (Fig. 3d).
For the strong sea ice forcing case, 138 members are needed,while for the weak sea ice forcing case, 200 members are notenough. We, thus, conclude that in December, more than;140 members are needed to single out with 95% conﬁdence
the sign of the forced stratospheric signal from the internalvariability when the sea ice forcing is strong, and more than
200 members are needed if the sea ice forcing is weak. If welower the conﬁdence level to 90%, ;90 members are needed
for strong SIC forcing, but still more than 200 members are
required for weak SIC forcing. Hence, the necessary number

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FIG. 4. Late November (21–30 Nov) EP ﬂuxes (arrows) and zonal-mean zonal wind (color shadings) under (a) strong sea ice forcing
and (e) weak sea ice forcing. The remaining panels are as in (a) and (e), but for (b),(f) early December (1–10 Dec), (c),(g) mid-December(11–20 Dec), and (d),(h) late December (21–30 Dec). The black contours denote ﬁeld signiﬁcance for zonal winds, and the cyan contours
denote the 5% local signiﬁcance based on the Student’s t test. The magenta vectors are at 5% signiﬁcance level based on the Student’s
t test for either vertical or meridional components.

for the weak SIC forcing runs is larger than recommended by
the PAMIP protocol (Smith et al. 2019).
While the polar cap Z provides an overview of the stratosphere–troposphere response to the sea ice forcing, further insight can be obtained by examining the spatial distributions of
the Z response. We ﬁrst investigate the December response
pattern of Z at 300 hPa (hereinafter Z300) and decompose it
into wavenumber-1 and wavenumber-2 components. Following the framework of constructive and destructive interference (e.g., Smith and Kushner 2012; Sun et al. 2015; Sun et al.
2022), we ﬁnd that for the wavenumber-1 component, bothstrong and weak SIC forcings give constructive interference(Fig. S6). For the wavenumber-2 component, strong SIC
forcing produces more constructive features than weak SIC

forcing, in particular over the northern European andSiberian sectors (Fig. S7). This analysis conﬁrms that thestrong SIC forcing gives rise to a circulation response patternin December that favors upward EP ﬂuxes and stronger
stratosphere–troposphere coupling (Smith and Kushner 2012).
We then consider the January–February response patterns of
Z at 50 hPa (hereinafter Z50), 500 hPa (hereinafter Z500),
and SLP (Fig. 5). In the stratosphere, a large and signiﬁcant
Z50 increase reaching 60 m occurs around Greenland forstrong sea ice forcing (Fig. 5a). By contrast, the Z50 response
to weak sea ice forcing extends over the whole polar region
with the maximum over Greenland but lacks ﬁeld signiﬁcance(Fig. 5b). Therefore, the stratospheric polar vortex weakening(or meandering from north to south) is more conﬁned in the

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FIG. 5. January–February mean Z50 responses under (a) strong sea ice forcing and (b) weak sea ice forcing. The remaining panels are as in (a) and (b), but for (c),(d) Z500 and (e),(f) SLP. The black dots denote the ﬁeld signiﬁcance,
while the cyan dots denote the 5% local signiﬁcance.

North Atlantic sector under strong sea ice forcing. In the middle troposphere, Z500 increases in both cases over the broader
region (Figs. 5c,d), but strong sea ice forcing gives rise tostronger Z500 increases above the North Atlantic and northern Siberia. Signiﬁcant responses are also seen in the SLP,

indicating nearly barotropic circulations, again with stronger
magnitude for strong sea ice forcing (Figs. 5e,f). The dipolar
SLP pattern in the North Atlantic largely resembles a negative
NAO pattern, which has imprints on the wind and precipitation (Hurrell et al. 2003; section 3b). Over northern Siberia,

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enhanced SLP appears in both weak and strong sea ice forcing
cases (Figs. 5e,f). For strong sea ice forcing, the increased SLP
extends from eastern Europe to northeastern Siberia, penetrating into central China. In contrast, weak sea ice forcing
produces SLP increases mostly concentrated in the Ural
Mountain regions, resembling a Ural blocking circulation(Peings 2019).
To summarize, in our CESM2-WACCM6 simulations, astronger stratosphere–troposphere coupled response duringlate autumn and winter is produced by strong sea ice forcing
compared to weak sea ice forcing. Sea ice loss reduces SLP
during November over the Arctic and excites more upward
planetary waves into the stratosphere. Then, via wave–mean
ﬂow interactions, the convergence of wave activity reduces
the strength of the stratospheric polar vortex, peaking in
December. In January and February, the perturbed stratospheric signal gradually migrates downward throughout thetroposphere. Such a chain of events is not seen as robustly under weak sea ice forcing, perhaps because of the smaller signal-to-noise ratio.

b. Impacts on the near-surface circulation, precipitation,and temperature
After the stratospheric polar vortex is perturbed, it can trigger subsequent downward migration and affect surface climate (Baldwin and Dunkerton 2001). In our simulations,
indeed, after the stratospheric polar cap weakens during
December, the signal gradually descends to the lower troposphere and near surface in January and February, affecting
zonal wind at 850 hPa (U850 hereinafter), precipitation, andsurface air temperature (SAT) as shown in Fig. 6. We ﬁrst focus on the North Atlantic sector where the January–February
mean responses are of largest magnitude and statistical signiﬁcance. Consistent with the negative NAO-like responses
shown earlier (Figs. 5e,f), U850 is displayed toward the northof polar jet stream associated with weakened polar vortex(Kidston et al. 2015) and weakened in the downstream regions of the mean eddy-driven jet, and possibly results in reduced storm-track activity (Seierstad and Bader 2009; Harvey
et al. 2014), as evidenced by a precipitation decrease over theeastern North Atlantic. The response patterns of U850 andtotal precipitation are largely similar in the weak sea ice forcing case, but with smaller magnitude and less statistical signiﬁcance (Figs. 6b,d).
Localized warming occurs above large sea ice loss regions,
including the Barents–Kara Seas, the western GreenlandSea, Hudson Bay, the Bering Strait, and the Sea of Okhotsk(Figs. 6e,f). In these regions, the total precipitation also increases (Figs. 6c,d) as a result of enhanced evaporation due tomore open water. Again, the amplitudes are stronger for strong sea
ice forcing. We also notice that the “warm Arctic–cold Eurasia”
pattern, suggested to be produced by stratosphere–troposphere
coupling (e.g., Zhang et al. 2018; Xu et al. 2021), does not appear
in the SAT responses to sea ice loss in our CESM2-WACCM6
simulations (Figs. 6e,f).
It is noted that, although these near-surface responses are
consistent with the imprints of the downward migration of

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stratospheric responses, they could also contain confounding
effects due to the thermodynamical and dynamical processes
within the troposphere, as well as concurrent sea ice loss impacts in late winter and early spring.

c. Modulation by AMV and IPV states
The macroperturbations of the ocean initial conditions in
our experiments allow us to investigate whether certain AMV
and IPV combined states affect the coupled stratosphere–
troposphere response to sea ice loss and subsequent impacts
on near-surface climate. We combine the two 100-member ensemble atmospheric responses for the strong and weak SIC
forcing cases initialized with the AMV1/IPV2 state to increase the ensemble size to 200 (and similarly for the AMV2/
IPV1 state), because almost no 5% signiﬁcant difference between ensemble-mean responses for strong and weak forcings
can be found with only 100 ensemble members, consistent
with the threshold in Fig. 3. To validate the mixture of thestrong and weak responses and the linear additivity, we compare the two sets of circulation responses. The spatial distributions of them are largely similar (Figs. S8–S10). Thus, weinterpret the 200-member combined response as the“median” response.
Figures 7a and 7b show the ensemble-mean polar cap Z responses to sea ice loss under the AMV1/IPV2 and AMV2/
IPV1 states, respectively. The AMV1/IPV2 state gives rise
to a statistically robust downward migration during late
December and January (Fig. 7a), whereas the AMV2/IPV1
state does not. Neither state exhibits clear upward migrationof tropospheric signals to the stratosphere in November andearly December. We also look at the difference between theensemble-mean polar cap Z responses under the AMV1/
IPV2 and AMV2/IPV1 states (Fig. 7c) to investigate the responses that are distinct in the two different oceanic background states. No statistically signiﬁcant difference occurs inthe stratosphere between November and most of March, except for a broad lowering between late January and earlyFebruary. However, there are more signiﬁcant signals in thelower troposphere, suggesting that the modulation effect particularly matters in the lower troposphere in the sense of polar-cap
average. Nonetheless, the polar-cap averages could overlook the
spatial structure of stratospheric and tropospheric responses andtheir coupling. Thus, we next investigate the modulation by
AMV and IPV states of the spatial patterns of Z50, Z500, and
SLP responses to sea ice loss (Fig. 8).
The AMV1/IPV2 state produces two extended Z50 ridge
patterns over the North Atlantic and northeastern Europe(Fig. 8a), which descend to 500 hPa (Fig. 8d) and to thenear-surface level to affect SLP (Fig. 8g). However, the SLP
responses over Siberia are statistically insigniﬁcant. The
SLP responses are also less signiﬁcant than Z500 or Z50
over the North Atlantic, indicating that the signals becomeless robust and weaker in the near surface than in the middle troposphere and stratosphere. The AMV2/IPV1 state
gives rise to SLP responses with more statistical signiﬁcancein the North Atlantic (Fig. 8h), which resemble the negative
NAO pattern, and extends to the middle troposphere at

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FIG. 6. January–February mean U850 responses under (a) strong sea ice forcing and (b) weak sea ice forcing. The remaining
panels are as in (a) and (b), but for (c),(d) total precipitation (convective plus large-scale) and (e),(f) SAT. The black dots denotethe ﬁeld signiﬁcance, while the cyan dots denote the 5% local signiﬁcance. The black contours in (a) and (b) denote the climatological mean U850 (contour interval of 4 m s21; solid contours for positive values and dashed contours for negative values).

500 hPa (Fig. 8e) and the lower stratosphere (Fig. 8b). The
corresponding U850 and total precipitation are shown in
Fig. 9, which also indicates that the SAT response is notstrongly modulated by AMV/IPV states, with similar SAT

responses localized to the regions of maximum sea ice loss
(Figs. 9g,h), and differences that are primarily signiﬁcant
over the North Atlantic subpolar gyre, consistent with apositive AMV state.

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FIG. 7. Evolution of polar cap–averaged Z response to Arctic sea ice loss from 1 Sep to 1 Aprilof the following year under (a) AMV1/IPV2 and (b) AMV2/IPV1 states. Here, the responses
to strong and weak sea ice forcings are combined. (c) The difference between (a) and (b). The
black contour lines denote the ﬁeld signiﬁcance, while the cyan contour lines denote the 5% local
signiﬁcance.

The response difference plots with statistical signiﬁcance reveal that the most critical circulation response associatedwith different oceanic background states resides in the North
Atlantic sector, particularly in the middle and lower troposphere (Figs. 8f,i). The AMV1/IPV2 state tends to produce
stronger Z500 and SLP in the North Atlantic, along with aslightly northward shift and strengthening of Atlantic jet(Fig. 9c) and reduced precipitation west of Spain (Fig. 9f).
The North Atlantic, thus, is the key region where different
oceanic background states could effectively modulate the atmospheric responses to Arctic sea ice loss.

d. Model dependence
Although the sequence and processes of stratosphere–
troposphere coupling and surface climate impacts forced by
Arctic sea ice loss are dynamically feasible and statisticallysigniﬁcant in CESM2-WACCM6, they could differ in otherclimate models, as suggested in previous studies (e.g., Screen
et al. 2018; Smith et al. 2019). We thus consider another set ofsimulations produced with IPSL-CM6A-LR, which follow thesame PAMIP protocol (Simon et al. 2022). In this model, thestratosphere–troposphere coupling and the enhanced polar
cap Z are also clearly detected under strong sea ice forcing,

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321

FIG. 8. January (a),(b) Z50, (d),(e) Z500, and (g),(h) SLP responses to sea ice forcing during AMV1/IPV2 and AMV2/IPV1 states,
respectively. Also shown are (c) the difference between (a) and (b); (f) the difference between (d) and (e); and (i) the difference between(g) and (h). Here, the responses to strong and weak sea ice forcings are combined. The black dots denote the ﬁeld signiﬁcance, while thecyan dots denote the 5% local signiﬁcance.

but the polar cap Z perturbation in the stratosphere peaks
during February (Fig. 10a), much later than the December
peak in CESM2-WACCM6 (Fig. 4a). There is also no evidence of stratosphere–troposphere coupling under the weaksea ice forcing (Fig. 10b). Therefore, the temporal evolutionof the stratosphere–troposphere coupling forced by sea ice
loss is subject to substantial model dependence, possibly due
to different mean states or seasonality of stratospheric andtropospheric circulations.
We also repeat our random resampling analysis for the
IPSL model. Figure 10c shows the distributions of resampledensemble means of Z10 for the strong and weak sea ice

forcing cases. The strong sea ice forcing results in an 83-m increase in Z10, with 99% of the resamples larger than zero(the red distribution in Fig. 10c). As the 2xSD of the resamples is 66.8 m, the signal-to-noise ratio is 1.24, slightly larger
than the ratio 1.18 for strong sea ice forcing in CESM2WACCM6. We also ﬁnd that the minimum ensemble size
needed for the 2xSD lower bound to be larger than zero is
133 (Fig. 10d), which is very close to the value of 138 fromCESM2-WACCM6. This conﬁrms that, in order to emerge
beyond the level of internal variability at the 5% signiﬁcancelevel, the stratospheric signal under strong sea ice forcing requires about 140 members in both models. As noted above,

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FIG. 9. January (a),(b) U850, (d),(e) total precipitation, and (g),(h) SAT responses to sea ice forcing during AMV1/IPV2 and AMV2/
IPV1 states, respectively. Also shown are (c) the difference between (a) and (b); (f) the difference between (d) and (e); and (i) the difference between (g) and (h). The values in (i) are multiplied by a factor of 3 for better illustration. Here, the responses to strong and weaksea ice forcings have been combined. The black dots denote the ﬁeld signiﬁcance, while the cyan dots denote the 5% local signiﬁcance.
The black contours in (a) and (b) denote the climatological mean U850 (contour interval of 4 m s21; solid contours for positive values anddashed contours for negative values).

the stratospheric response under weak sea ice forcing issmall and lacks statistical signiﬁcance in IPSL-CM6A-LR(the green distribution in Fig. 10c), with a signal-to-noise ratio of only 0.07.
The spatial distributions of the February Z50, Z500, and SLP
responses simulated by IPSL-CM6A-LR are shown in Fig. 11.
The coupled stratosphere–troposphere response occurs mostly
within the Arctic Circle under strong sea ice forcing (Figs. 11a,c),different from that simulated by CESM2-WACCM6 where it

occurs mainly over the North Atlantic sector (Figs. 5a,c). Moreover, the coupled response does not descend to the near surface
as no statistically signiﬁcant SLP responses appear (Fig. 11e).
Correspondingly, the U850 and precipitation responses are notrobust (Figs. 12a,c). Like CESM2-WACCM6, the SAT responses
are localized to the areas of maximum sea ice loss (Fig. 12e). Under the weak SIC forcing, no clear stratosphere–troposphere coupled response occurs (Figs. 11b,d,f) and no robust near-surface
climate responses exist (Figs. 12b,d,f).

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FIG. 10. As in Fig. 4, but using IPSL-CM6A-LR. Note that (c) and (d) are based on February Z10 responses [indicatedby dashed yellow lines in (a) and (b), respectively].

4. Discussion
The results presented in this study support previous ﬁndings that Arctic sea ice loss can inﬂuence midlatitude surface
climate via a stratospheric pathway (Jaiser et al. 2013; Cohenet al. 2014; Kim et al. 2014; Jaiser et al. 2016; Nakamura et al.
2016; Ruggieri et al. 2016). However, some aspects warrant
further discussion. The ﬁrst issue relates to the methodology
used to constrain sea ice in coupled model experiments, as the
PAMIP protocol did not require a uniﬁed approach, and different methods may produce varied results as suggested basedon idealized model conﬁgurations (England et al. 2022). In our
CESM2-WACCM6 simulations, we utilize a nudging techniqueto constrain the sea ice condition toward the PAMIP target.
However, this sea ice nudging technique has been suggested to
produce a spurious warming over the Northern Hemisphere inboth idealized models and GCMs that may cause an overestimation of the climate responses to sea ice loss (England et al.
2022). In addition, varying sea ice thickness can contribute torecent Arctic warming (Gerdes 2006; Lang et al. 2017; Labeet al. 2018), but sea ice thickness was constrained to be 2 m inour experiments, as speciﬁed in the PAMIP protocol. It is notedthat the IPSL-CM6A-LR only constrains SIC, not SIV. This
could lead to a different sea ice thickness proﬁle from that of
CESM-WACMM6 and contribute to the different degrees ofArctic warming. In addition, several recent studies showed thatthe stratospheric polar vortex could also directly affect the
Arctic sea ice, allowing for two-way interactions between thestratospheric vortex and sea ice (e.g., Smith et al. 2018; Kelleher
et al. 2020; J. Zhang et al. 2022). In our simulations, as the sea
ice condition is nudged, this effect cannot be included. Further modeling efforts are needed to improve our understanding of this aspect and its contribution to midlatitude
circulation changes.
The next issue pertains to the inﬂuence of internal variability (i.e., the signal-to-noise ratio). In particular, Sun et al.
(2022) showed that two different 100-member ensemble averages of stratospheric (10 hPa) zonal-mean zonal wind response to PAMIP sea ice forcing can be of opposite sign
because the signal-to-noise ratio in their experiment was verysmall. In our CESM2-WACCM6 simulations with the signalto-noise ratio of 1.18 for December Z10 in response to thestrong sea ice forcing, we do not ﬁnd such stark contrast between the ﬁrst (dotted vertical line in Fig. 4c) and second(solid vertical line in Fig. 4c) 100-member averages of the
December Z10 stratospheric response, both of which lie close
to the 200-member average, and indeed within one standard
deviation of the full distribution. We also calculate the middle
100-member (i.e., 51–150 members) average (dashed verticalline in Fig. 4c), and it is again close to the 200-member average. While the IPSL-CM6A-LR simulations show a wider

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FIG. 11. As in Fig. 5, but using IPSL-CM6A-LR and for February.

range between the discrete 100-member averages of February
Z10 (vertical lines in Fig. 10c), they are still not as extreme asin Sun et al. (2022), because the IPSL-CM6A-LR simulations
had the signal-to-noise ratio of 1.24 for February Z10. Moreover, Sun et al. (2022) analyzed atmosphere-only simulations,which do not allow two-way coupling between the atmosphere, ocean, and sea ice. Previous studies have indicatedthat including atmosphere–ocean dynamical coupling can amplify the atmospheric response to Arctic sea ice loss (Deseret al. 2015, 2016) and therefore enhance the signal-to-noise ratio. Also, Sun et al. (2022) used CAM6 with low-top conﬁguration, in which the frequency of sudden stratospheric warming
events is underestimated (Ayarzagüena et al. 2020), whereas
both CESM2-WACCM6 and IPSL-CM6A-LR have a high-top

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FIG. 12. As in Fig. 6, but using IPSL-CM6A-LR and for February.

conﬁguration. In addition, Smith et al. (2022) reported
that, by looking into multimodel means in December–
February, weak but reduced EP ﬂuxes appear. This is different from our single-model results in a given Decemberor February and deserves further analysis to uncover the
essential reason.

Compared with observation/reanalysis data-based studies,
our modeling results show apparent discrepancies in time andspace. For example, Kim et al. (2014) and Romanowsky et al.
(2019) used reanalysis data to show that the largest stratospheric response occurs in January and February, later than thestratospheric response of our CESM2-WACCM6 simulations,

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which already emerges in December. Also, Furtado (2019)
found that blocking in the Ural Mountain and Scandinavia wasa tropospheric precursor of stratospheric response. However,no such patterns present in our simulations; instead, only reduced SLP over the pan-Arctic domain emerges. Therefore, itwould be important to understand why these spatiotemporal
discrepancies between observations/reanalysis data and GCM
simulations occur.
Last, previous modeling studies showed that an AMV1state tends to give a negative NAO-like response pattern during boreal winter (Peings and Magnusdottir 2016; Elsbury
et al. 2019; Kwon et al. 2020). Instead, comparing January–
February SLP responses in our AMV1/IPV2 and AMV2/
IPV1 ensemble means, the former produces a less coherent
negative NAO-like pattern in response to sea ice decrease
than the latter (cf. Figs. 8a,b). This is likely caused by a more
southerly eddy-driven jet position, which is associated with
the NAO negative phase preferred by the AMV1 state, inhibiting a strong midlatitude tropospheric circulation response to the sea ice decline (Smith et al. 2017). It is also
possibly due to the concurrent impacts of AMV1 and IPV2,
which leads to positive and negative NAO-like patterns, respectively (Elsbury et al. 2019). Thus, the negative NAO signal from AMV1 may be somewhat offset by that from IPV2
in the AMV1/IPV2 case. In addition, we cannot rule out thenonlinear interplay between AMV and IPV and the associated impact on the stratospheric and tropospheric responses(e.g., Davini et al. 2015; Ruprich-Robert et al. 2017; Sun et al.
2017; Elsbury et al. 2019; Meehl et al. 2021). It is also notedthat our calculation of the AMV index could introduce spurious climatic responses (Frankignoul et al. 2017; Deser andPhillips 2023). Whether or not this artiﬁcial effect can inﬂuence the atmospheric responses to Arctic sea ice loss deserves
further investigation.

5. Summary
This study uses the high-top CESM2-WACCM6 coupledmodel, for the ﬁrst time, to conduct time-slice experiments
following the PAMIP protocol (Smith et al. 2019), different
from previous studies that used atmosphere-only models (e.g.,
Xu et al. 2021; Zhang and Screen 2021; Sun et al. 2022). The
sea ice conditions are constrained to preindustrial, present,
and future states throughout a 14-month integration period inorder to examine the large-scale circulation responses in thestratosphere and troposphere under varying strengths ofArctic sea ice loss. We consider the difference between the future and preindustrial runs as the response under strong sea
ice forcing, and that between the future and present runs as
the response under weak sea ice forcing. We focus on thestratospheric response and the subsequent near-surface impacts: that is, the so-called stratospheric pathway linking
Arctic climate change to midlatitude impacts. As recent studies found that the recommended ensemble size in the PAMIP
protocol (100 members) is too small to robustly distinguishthe signal from sea ice forcing from the noise of internal variability (e.g., Labe et al. 2019; Liang et al. 2020; Peings et al.
2021; Sun et al. 2022), we have enlarged our set of simulations

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to 200 members. To investigate whether different backgroundocean states can modulate aspects of the response to Arctic sea
ice loss, we initialized half of the ensemble members (i.e., 100
members) with the AMV1/IPV2 state and the other half with
the AMV2/IPV1 SST patterns.
Following the classic framework to analyze stratosphere–
troposphere coupling (Baldwin and Dunkerton 2001), we
have shown that Arctic sea ice loss produces signiﬁcant precursor SLP structures during November with decreased values over the Arctic Ocean and increased values over North
Atlantic and Eurasia, together with enhanced upward wave
activity into the stratosphere. Via wave–mean ﬂow interaction, the resultant wave drag weakens the stratospheric polar
vortex and elevates the polar cap geopotential height inDecember. Then, in January–February, the perturbed
stratospheric circulation gradually migrates downward to
the lower troposphere with a negative NAO-like pattern
and an enhanced ridge near the Ural Mountains, affecting
the magnitude and location of the tropospheric eddydriven jet and precipitation in Eurasia. The stronger thesea ice forcing, the greater the stratosphere–troposphereresponses (e.g., polar cap Z and negative NAO) and thebroader their spatial extents (e.g., high SLP extending into
central China).
In light of the low signal-to-noise of the simulated response
to Arctic sea ice loss (e.g., Labe et al. 2019; Liang et al. 2020;
Peings et al. 2021; Sun et al. 2022), we use a random samplingtechnique to robustly quantify the effect of internal variabilityon the stratospheric response in December, when the response reaches its peak in our CESM2-WACCM6 simulations. We show that the ensemble-mean Z10 response is twiceas large under strong sea ice forcing than weak sea ice forcing.
On the other hand, the amplitude of internal variability, measured by two standard deviations of the probability density
function of December polar cap Z10, is not substantiallyaffected by the magnitude of sea ice loss: hence, the signalto-noise ratio of the response increases by approximately afactor of 2 (1.18 vs 0.66) under strong versus weak sea ice
forcing. We also ﬁnd that 130–140 members are needed to distinguish the stratospheric response from the noise of internalvariability with 95% conﬁdence for strong sea ice forcing,while 200 members do not sufﬁce for weak sea ice forcing. A
similar ensemble size requirement is found for the experiments with IPSL-CM6A-LR in the strong sea ice forcing case.
To investigate whether AMV and IPV states modulate theresponse to sea ice loss, which is the major novelty of thisstudy, we combine the 100 members of the weak and strong
sea ice forcing cases to obtain a total of 200 members underthe AMV1/IPV2 and AMV2/IPV1 states. In the sense ofpolar-cap average, the AMV1/IPV2 state favors downward
migration of stratospheric responses to the near surface during late December and January, while the AMV2/IPV1
shows less coupling between the stratosphere and tropospherebut stronger tropospheric responses. The surface impacts inJanuary modulated by AMV and IPV states are apparent in
the North Atlantic sector, manifested as the negative NAOlike circulation pattern in the middle and lower troposphere,
precipitation decrease, and weakened or shifting of the Atlantic

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jet stream. The AMV1/IPV2 state tends to shift the weakened
jet stream toward northern Europe. On the other hand, varying
AMV and IPV states do not seem to strongly modulate thenear-surface temperature response to sea ice loss. It is notedthat more ensemble members are appreciated for the weak andstrong cases to conﬁrm that the above ﬁndings, based on the
mixture of the two, are robust.
We also considered parallel sets of PAMIP experimentswith the IPSL-CM6A-LR coupled model to investigate if
stratosphere–troposphere coupling forced by Arctic sea ice
loss is model dependent. The maximum stratospheric response
simulated by IPSL-CM6A-LR occurs in February instead of
December as in CESM2-WACCM6. The stratospheric responses with IPSL-CM6A-LR only descend to the middle troposphere, but not to the near surface, distinct from stronger
stratosphere and near-surface coupling simulated by CESM2WACCM6. Model dependence requires further studies, as thetiming of the strongest stratospheric response and the extent
of stratosphere–troposphere coupling are different in the twomodels.
Taken together, our ﬁndings support the notion that Arctic
sea ice loss is capable of inﬂuencing midlatitude climate via astratospheric pathway on seasonal time scales, although thesimulated circulation responses have a relatively low signal-tonoise ratio and require strong sea ice forcing (and large ensemble sizes) to be detectable. Our ﬁndings further highlight thatmodulations by the background oceanic mean state and model
dependence need to be taken into account.
Acknowledgments. We thank two anonymous reviewers
and the editor for constructive comments and suggestions
to improve the quality of this manuscript. We acknowledge
support by the BlueAction Project (the European Union’s
Horizon 2020 research and innovation programme, #727852,
http://www.blue-action.eu/index.php?id53498). The WHOI groupwas supported by the U.S. National Science Foundation(NSF) Ofﬁce of Polar Programs Grants 1736738/1737377 and2106190/2106228. L. Sun is supported by the National Science Foundation (AGS-2300038). Y.-C. Liang was supportedby the grants of the National Science and Technology Council (MOST 110-2111-M-002-019-MY2, 111-2628-M-002-011, and112-2628-M-002-009) to National Taiwan University. NCAR
was sponsored by the National Science Foundation. We
would like to acknowledge the use of computational resources (https://doi.org/10.5065/D6RX99HX) at the NCAR–
Wyoming Supercomputing Center provided by the National
Science Foundation and the State of Wyoming and supportedby NCAR’s Computational and Information Systems Laboratory. For IPSL-CM6A-LR simulations, this work was granted
access to the HPC resources of TGCC under the allocations
A7-017403 and A11-017403 made by GENCI. The details of
IPSL-CM6A-LR can be found in https://cmc.ipsl.fr.
Data availability statement. The PAMIP data with CESM2WACCM6 and IPSL-CM6A-LR can be obtained on the Earth
System Grid Federation website (https://doi.org/10.22033/ESGF/
CMIP6.2197) or upon request to the corresponding author.

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