Zhou et al.
Progress in Earth and Planetary Science(2023) 10:59
https://doi.org/10.1186/s40645-023-00591-x

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessInterannual variation of the Warm Arctic–
Cold Eurasia pattern modulated by Ural
blocking and the North Atlantic Oscillation
under changing sea ice conditions
Xiling Zhou1* , Tomonori Sato2 and Shixue Li1

Abstract
Together with rapid Arctic warming and sea ice decline, especially over the Barents–Kara seas (BKS), extreme cold
winters have occurred frequently in mid-latitudes, particularly in Central Eurasia. A pattern with two distinct wintertemperature anomalies centered over the BKS and Central Eurasia is known as the Warm Arctic–Cold Eurasia (WACE)
pattern. The impacts of sea ice loss over the BKS and internal atmospheric variability on past WACE formation remain
under discussion mainly due to the large internal atmospheric variability in the mid-latitudes. This study analyzed
a large-ensemble historical experiment prescribing observed sea ice condition to investigate the role of internal
atmospheric variability in the observed interannual variation of the WACE pattern. Comparison of ensemble members suggests that internal atmospheric variability is important for regulating the magnitude of the WACE pattern.
Besides the strong effect of local sea ice loss, winter temperature over the BKS increases due to warm advection
driven by the Ural blocking and positive phase of the North Atlantic Oscillation. A decrease in winter temperature
over Central Eurasia is mainly attributable to the cold advection enhanced by Ural blocking rather than the remote
effect of sea ice decline over the BKS. Our study reveals the importance of internal atmospheric variability in elucidating the observed interannual variation of the WACE pattern.
Keywords Warm Arctic–Cold Eurasia pattern, Sea ice loss, Ural blocking, North Atlantic Oscillation
1 Introduction
Synchronous with rapid Arctic warming, the sea ice area
has declined dramatically over the Arctic Ocean, especially over the Barents–Kara seas (BKS) since the 1990s(Stroeve and Notz 2018; Isaksen et al. 2022; Rantanen
et al. 2022), enhancing turbulent heat flux from the surface to the atmosphere (Kim et al. 2019). Nevertheless,*Correspondence:
Xiling Zhou
zhouxiling@ees.hokudai.ac.jp
1
Graduate School of Environmental Science, Hokkaido University,
N10W5, Sapporo, Hokkaido 060‑0810, Japan
2
Faculty of Environmental Earth Science, Hokkaido University, N10W5,
Sapporo, Hokkaido 060‑0810, Japan

extreme cold winters with increased freezing days anddecreased minimum temperatures have occurred frequently in mid-latitudes, especially in Central Eurasia(CEU) (Cohen et al. 2014; McCusker et al. 2016; Johnson
et al. 2018). This pattern with two distinct winter temperature anomalies centered over the BKS and CEU is
known as the Warm Arctic–Cold Eurasia (WACE) pattern, which is characterized as the second leading modeof winter surface air temperature (SAT) over Eurasia(Mori et al. 2014). Despite the dedicated research effort
in recent years on the causes of both the frequent cold
winters in Eurasia and the WACE pattern, the mechanism linking the Arctic and the mid-latitude winter climate remains to be fully elucidated (e.g., Inoue et al.
2012; McCusker et al. 2016; Sun et al. 2016; Zhang et al.

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Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

2018; Blackport et al. 2019; He et al. 2020; Wang andChen 2022).
It has been proposed that sea ice decline over the BKS
has influenced the recent cold winters in Eurasia (e.g.,
Inoue et al. 2012; Mori et al. 2019), and that modulation of cyclone tracks and the troposphere-stratosphere
pathway is responsible for the remote effect of sea ice
loss (Inoue et al. 2012; Kim et al. 2014; Nakamura et al.
2015; Zhang et al. 2018). Inoue et al. (2012) suggested
that lower baroclinicity over the Barents Sea in low ice
conditions reduces the eastward progression of cyclones,
which leads to an anticyclonic anomaly over the Siberian coast that causes anomalous warm advection overthe Barents Sea and cold advection over eastern Siberia.
Zhang et al. (2018) pointed out that the downward influence of the stratospheric circulation anomaly, in response
to late autumn sea ice loss, intensifies the ridge near the
Ural Mountains and the trough over East Asia, favoringcold winters in Siberia. Nakamura et al. (2015) proposedthat recent sea ice reduction in late autumn is linked to
the negative phase of the Arctic Oscillation/North Atlantic Oscillation (NAO) in winter, resulting in cold advection toward mid-latitudes.
Some studies have suggested that sea ice decline is
not a crucial factor for cold winters in Eurasia (e.g.,
McCusker et al. 2016; Blackport et al. 2019; Blackportand Screen 2021), and that the formation of the WACE
pattern is mainly attributable to internal atmosphericvariability (e.g., Li et al. 2015; Sun et al. 2016; Ogawa et al.
2018; He et al. 2020). The negative interannual correlation between winter sea ice over the BKS and cold winters in CEU arises primarily because both are driven by
the same anticyclonic circulation anomalies over the Ural
region (Blackport et al. 2019). The Ural blocking (UB) isessential in inducing the WACE pattern, given that the
BKS and CEU are influenced by warm and cold advection, respectively (Tyrlis et al. 2020). He et al. (2020)
suggested that increased moisture and energy advection
from the North Atlantic are necessary features when the
WACE pattern is generated.
Previous studies based on observation data and modelexperiments have reported diverse results about the roleof sea ice loss and internal atmospheric variability in theformation of the WACE pattern. The diversity is mainly
attributable to the large internal atmospheric variability in the mid-latitudes and the difficulties in separating cause from effect (Screen et al. 2014; Blackport andScreen 2021). To address this issue, a large-ensemble
simulation of an atmospheric general circulation model(AGCM) with perturbed initial conditions is useful, since
it presents possible internal atmospheric variability realized under the identical lower boundary condition. This
benefit helps distinguish the role of internal atmospheric

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variability in forming the WACE pattern. Therefore, thefirst objective of this study is to investigate the role ofinternal atmospheric variability in the observed interannual variation of the WACE pattern using a large-ensemble historical experiment.
Although some studies have already pointed out thatseveral internal atmospheric variability processes are
essential for the formation of the WACE pattern (e.g.,
Luo et al. 2016b; Blackport et al. 2019), their combined
effect leading to the WACE pattern has not been quantitatively studied yet, owing to the limited sample size inobservation. Hence, the second objective of this study isto quantitatively analyze the combined effect of internal
atmospheric variability on the WACE pattern. The findings of this study are expected to improve the skill in predicting extreme cold weather over Eurasia by developinga deeper understanding of the linkage between the Arctic
and the mid-latitude climate.

2 Data and methods
2.1 Data

The monthly temperature, sea-level pressure (SLP), wind,surface heat flux, and 6-hourly geopotential height data
used in this study were obtained from the European Centre for Medium-Range Weather Forecasts Reanalysis v5
with horizontal resolution of 0.25° × 0.25° (Hersbach et al.
2020). The observed monthly sea surface temperature(SST) and sea ice concentration (SIC) data were obtained
from Centennial in situ Observation‐Based Estimatesof SST version 2 (COBE-SST2) with horizontal resolution of 1° × 1° (Hirahara et al. 2014). For convenience, thereanalysis data are regarded as observation data in theremaining discussion.
This study used data from the “Database for Policy
Decision-Making for Future Climate Change,” producedby the Meteorological Research Institute AGCM version
3.2 with a high-resolution grid interval of 60 km (Mizuta
et al. 2017). The large-ensemble size and the long-term
simulation of this dataset provide the opportunity toanalyze internal atmospheric variability. We adopted ahistorical experiment (hereafter, HIST) spanning 1951–
2020, which was conducted by prescribing the observed
SST and SIC from COBE-SST2 as the lower boundary
conditions, and the observed greenhouse gases, aerosols, and ozone as external forcing (Mizuta et al. 2017;
Imada et al. 2020). To analyze HIST for the period withreliable observation data, we focused our study on
1979–2020. HIST contains 100 members with perturbed
initial conditions and boundary conditions. Therefore,
the ensemble-mean can be considered as the responseto the external forcing. In contrast, the variation among
the ensemble members can be taken as a reflection of
the internal atmospheric variability of the climate system

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59(i.e., the land–atmosphere system excluding the ocean
component).
2.2 Method

We applied empirical orthogonal function (EOF) analysis separately to the observed and simulated winter(December–February) latitude-weighted SAT anomalies
over Eurasia (20°–90° N, 0°–180° E) during 1979/1980–
2019/2020 to extract the WACE pattern and its index.
Overall, 4100 snapshots concatenated by 100 members
over 41 years of simulated winter SAT anomalies wereanalyzed. Then, their corresponding principal components (PC) were rearranged on the time axis to present
the PC for each member. The first leading mode denotes
the continental warming pattern with positive SAT
anomalies over the Eurasian continent (Additional file 1:
Fig. S1a and S1c). The second leading mode denotes the
WACE pattern with positive and negative SAT anomalies over the BKS and CEU, respectively (Additional file 1:
Fig. S1b and S1d). The simulated WACE pattern is similar
to that observed, with a spatial correlation coefficient ofapproximately 0.86 (Additional file 1: Fig. S1b and S1d).
The time series of the observed and simulated second
leading mode are defined as the observed WACE index
and simulated WACE index for each member, respectively (Additional file 1: Fig. S1f and S1h).
Similarly, we also applied EOF analysis separately tothe observed and simulated winter latitude-weighted
SLP anomalies over the Atlantic sector (20°–80° N,
90° W–40° E) during 1979/1980–2019/2020 to determine
the winter NAO pattern and its index. The first leading
mode is defined as the winter NAO pattern, exhibiting opposite SLP anomalies over the Azores and Iceland(Additional file 2: Fig. S2a). The simulated winter NAO
pattern is very similar to the observed winter NAO pattern, with a spatial correlation coefficient of 0.98 (notshown). The time series of the observed and simulated
first leading modes are defined as the observed winter
NAO index and simulated winter NAO index for eachmember, respectively (Additional file 2: Fig. S2b).
We calculated the UB frequency following Tibaldi
and Molteni (1990), which considers the meridional
gradient of daily 500-hPa geopotential height over
40°–80° E corresponding to the Ural region. Firstly,
we calculated the daily northern meridional gradient(Z80◦ N+� − Z60◦ N+� )/((80◦ N + � ) − (60◦ N + � ))

and
daily
southern
meridional
gradient(Z60◦ N+� − Z40◦ N+� ) /((60◦ N + � ) − (40◦ N + � ))for each longitude. Here, Z is the 500-hPa geopotential
height and  varies from − 5°, 0°, to 5°. Secondly, blocking was detected on daily basis if the northern meridional gradient <  − 10 gpm per degree latitude and thesouthern meridional gradients > 0 for at least one of the

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, and such condition persists for at least three consecutive days. Finally, after the detection of the blocking days,
winter UB frequency was defined as the total blocking
days averaged over 40°–80°E in each winter (Additional
file 2: Fig. S2c). For observation and each member of theensemble simulation, the standardized winter UB frequency for 41 years is used as the winter UB index.

3 Results
3.1 Observed and simulated winter temperature changeover the Barents–Kara seas and Central Eurasia

The observed and simulated winter SAT and circulation
trends are shown in Fig. 1a and b, respectively. There isan observed warming trend centered over the BKS anda cooling trend over CEU, with positive surface pressure
and anticyclonic circulation trend over the Ural region(Fig. 1a). The ensemble-mean of the simulated winterSAT trend shows a similar but weaker warming trend
over the BKS (Fig. 1b). Because the ensemble-mean trend
could be considered as the trend due to external forcing,
results above suggest the external forcing largely causes
observed warming trend over the BKS. The positive surface pressure and anticyclonic circulation trend over the
Ural region, and the cooling trend over CEU are not seenin the ensemble-mean fields (Fig. 1b). This implies thatthe influence of internal atmospheric variability is large
in the observed cooling trend over CEU.
The interannual variation in winter SAT over the BKS
and CEU is shown in Fig. 1c and d, respectively. Both theobserved and the ensemble-mean of the simulated winter SAT over the BKS show a rapid increase, especiallyafter the late 1990s (Fig. 1c) when the winter sea ice inthis region started to decline rapidly, indicating a strong
effect of local sea ice loss on SAT (Kim et al. 2019). Nevertheless, the intermember spread of the simulated winter SAT trend over the BKS is not negligible, as shown in
Fig. 2, indicating that the effect of internal atmosphericvariability could also perturb the observed winter warming over the BKS. In contrast to the rapid warming overthe BKS, several observed cold winters occurred in CEU
around 2010 (Fig. 1d). The intermember spread of thesimulated winter SAT trend over CEU is very large, suggesting a crucial role of internal atmospheric variability
on observed winter cooling over CEU (Fig. 2).
3.2 Second leading mode of winter temperature
over Eurasia

The observed winter SAT trend in recent decades showsa WACE-like pattern (Figs. 1a and Additional file 1: S1b).
EOF analysis was applied separately to the observed andsimulated winter SAT anomalies over Eurasia to study
the interannual variation of the WACE pattern. The
second leading mode of the simulated winter SAT over

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

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Fig. 1 Observed and simulated winter temperature change over the BKS and CEU. a, b Linear trends of winter SAT (shading; °C/decade), SLP(contour; hPa/decade; 0.2 intervals with negative values shown by dashed lines), and 850-hPa wind (vector; m/s/decade) for observations (a)and 100-member ensemble mean (b) during 1979/1980–2019/2020. Areas indicated by the green and orange lines in (b) denote the locationsof the BKS (70°–80° N, 30°–70° E) and CEU (40°–60° N, 60°–120° E), respectively. c, d Interannual variation of observed and simulated winter SAT
over the BKS and CEU, respectively, during 1979/1980–2019/2020. Green and orange lines denote observed winter SAT over the BKS and CEU,
respectively; black lines denote the 100-member ensemble-mean; 25th–75th percentiles and 5th–95th percentiles of the ensemble spread are
indicated by dark and light shading, respectively

Fig. 2 Intermember spread of the simulated winter SAT trend. a The standard deviation of the 100-member simulated winter SAT trend (°C/decade). The green and orange regions denote the location of the BKS and CEU, respectively. b Observed (star) and simulated (box and whisker)
winter SAT trends over the BKS and CEU. The upper and lower whiskers of the box are the maximum and minimum values, respectively; the upperand lower sides of the box are the upper and lower quartiles, respectively. Horizontal solid lines across the boxes denote the 100-member
ensemble-mean

Eurasia exhibits a WACE structure, accounting for 14%of the total variance (Fig. 3a). The pattern shows a warm
anomaly centered over the BKS and a cold anomaly centered over CEU, with an intensified surface high and an

anticyclonic circulation over the Ural region (Fig. 3a).
Both the observed and the ensemble-mean of the simulated WACE index increase rapidly after the late 1990s,
suggesting a strong effect of the external forcing on theZhou et al. Progress in Earth and Planetary Science(2023) 10:59

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Fig. 3 Simulated WACE pattern and member selection. a Regression patterns of winter SAT (shading; °C), SLP (contour; hPa; 0.2 intervals
with negative values shown by dashed lines), and 850-hPa wind (vector; m/s) anomalies against the member-concatenated simulated WACE
index during 1979/1980–2019/2020. The specified value denotes the explained variance ratio. b Interannual variation of observed and simulated
WACE index. Blue and black lines denote the observed WACE index and WACE index for the 100-member ensemble mean, respectively; 25th–75th
percentiles and 5th–95th percentiles of the ensemble spread are indicated by dark and light shading, respectively. Numbers in parentheses
denote the WACE index trend (σ/decade) during the study period. c Histogram of the interannual correlation coefficient between the observed
WACE index and simulated WACE index for each member. Green and orange bars denote the 10 members of the high-correlation (HC) groupand the 10 members of the low-correlation (LC) group, respectively. d Interannual variation of WACE index for the observation, high-correlationgroup, and low-correlation group. The blue line denotes the observed WACE index; thin and thick green lines denote the 10 membersof the high-correlation group and their average, respectively; thin and thick orange lines denote the 10 members of the low-correlation groupand their average, respectively

observed WACE index (Fig. 3b). Simultaneously, the simulated WACE index shows a large intermember spread,indicating that internal atmospheric variability also
attributes to the observed interannual variation of the
WACE pattern (Fig. 3b).
It is crucial to establish the extent to which internal
atmospheric variability determines the observed interannual variation of the WACE pattern. To address thisproblem, two different groups of members extracted
from the 100 members were selected, considering thesimilarity in the observed WACE index. Prior to thisselection, the interannual correlation coefficient between
the observed WACE index and simulated WACE index
for each member was calculated, as summarized inFig. 3c. We selected the 10 members with the highest
correlation coefficient (r > 0.47) and the 10 members withthe lowest correlation coefficient (r < 0.11); hereafter, they
are referred to as the high-correlation group and the lowcorrelation group, respectively (Fig. 3c). The averaged

winter SAT of the high-correlation group well reproduces the observed interannual variation of the winterSAT over the BKS (r = 0.88) and CEU (r = 0.45) (Additional file 3: Fig. S3). Hence, the averaged WACE index
of the high-correlation group also shows high correlation
against the observation (r = 0.86) (Fig. 3d). Conversely,
the averaged winter SAT of the low-correlation group
reproduces the observed interannual variation of winterSAT over the BKS (r = 0.81) (Additional file 3: Fig. S3a),mainly because of the prescribed SST and SIC, while it
fails to reproduce the observed interannual variation of
the winter SAT over CEU (r = 0.05) (Additional file 3: Fig.
S3b). Hence, the averaged WACE index of the low-correlation group leads to low correlation against the observation (r = 0.11) (Fig. 3d). The diversity of simulated WACE
index between the two groups is derived mainly from thedifference in simulated winter SAT over CEU (Additional
file 3: Fig. S3b). Because both groups are prescribedby the observed sea ice condition but have different

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

performances in simulating winter SAT over CEU andthus different WACE index, the two groups were compared to investigate the role of internal atmospheric variability in the mechanism of the observed cold winters in
CEU and the formation of the WACE pattern.
3.3 Different circulation and temperature patterns related
to winter sea ice decline over the Barents–Kara seas

The influence of sea ice decline over the BKS on the formation of cold winters in Eurasia has been proposed bymany studies (e.g., Inoue et al. 2012; Mori et al. 2019).
This subsection investigates different circulation andtemperature patterns between the 10-member mean of
the high-correlation group and the 10-member mean of
the low-correlation group. Since the prescribed sea ice
condition was identical in the high-correlation group andlow-correlation group, the sea ice-induced atmosphericvariability can be regarded as nearly the same between
the two groups. The differences between the two groups
are regarded as a manifestation of internal atmosphericvariability.
Here, we study observed pressure and temperature patterns at the surface level related to winter sea ice decline.
The regression analysis on sign-reversed SIC over the
BKS shows higher surface pressure over the Ural region(Fig. 4a). It is also found that higher surface pressure over
the Northern Atlantic and lower surface pressure over
Iceland, indicating the presence of positive NAO. For

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the observed temperature pattern, there are positive andnegative SAT anomalies over the BKS and CEU, respectively (Fig. 4a).
According to the similar regression analysis for thehistorical experiment, the high-correlation group showsa pattern similar to that of the observation, although thepositive surface pressure anomaly over the Ural region
and negative SAT anomaly over CEU are weaker in thehigh-correlation group (Fig. 4b). In contrast, the lowcorrelation group shows lower surface pressure over the
Ural region, absence of positive NAO, and positive SAT
anomaly over CEU (Fig. 4c).
The difference between the two groups, which isregarded as a manifestation of internal atmosphericvariability, shows clear positive NAO phase and higher
surface pressure over the Ural region, with positive andnegative SAT anomalies over the BKS and CEU, respectively (Fig. 4d). This indicates the observed cold CEU is
better reproduced when internal atmospheric variability
resembles observation.
In the same way, the circulation and horizontal temperature advection pattern at the 850-hPa related to
winter sea ice decline is investigated in Fig. 5. The circulation pattern at this level corresponds well with thesurface pressure pattern (Figs. 4 and 5). The horizontal
temperature advection pattern in the high-correlationgroup is similar to the observation, with a warm andcold advection over the BKS and CEU, respectively

Fig. 4 Winter circulation and SAT patterns related to reversed winter sea ice change over the BKS. a Regressed observed winter SAT (shading; °C)
and SLP (contour; hPa; 0.4 intervals with negative values shown by dashed lines) anomalies on observed winter SIC over the BKS. Note that SIC
was sign-reversed and normalized. b, c Similar to (a), but (b) is for the 10-member mean of the high-correlation group, c is for the 10-member meanof the low-correlation group. d The difference between (b) and (c). Dots in a–c denote shading statistically significant at the 0.05 level by Student’s
t testZhou et al. Progress in Earth and Planetary Science(2023) 10:59

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Fig. 5 Winter horizontal temperature advection pattern related to reversed winter sea ice change over the BKS. Similar to Fig. 4, but for winterhorizontal temperature advection (shading; °C/day) and wind (vector; m/s) at 850-hPa. Dots in a–c denote shading statistically significant at the 0.05
level by Student’s t test. The grey shadings in denote the topography(Fig. 5a and b). On the contrary, the temperatureadvection pattern over the two regions is very weak oreven reversed in the low-correlation group (Fig. 5c).
The difference between the high-correlation groupand low-correlation group shows anticyclonic circulation anomalies over the Ural region and positive NAO(Fig. 5d). Their coincidence promotes warm advection,
which is maintained mainly by meridional component,
and increases the temperature over the BKS (Figs. 5dand Additional file 4: S4d). These features agree with
Luo et al. (2017), which suggested this circulation pattern promotes the transport of moisture and heat fromthe North Atlantic near the Gulf Stream extension
region, where SST is high, toward the BKS. Simultaneously, the northerly wind that dominates to the east of
the Ural region enhances cold advection from the BKS
to CEU, leading to a temperature decrease over CEU(Figs. 5d and Additional file 4: S4d). This result underscores the importance of internal atmospheric variability for forming the observed cold winters in CEU
and the WACE pattern.
The southerly wind anomaly heading to BKS appears
not to contribute to the sensible and latent heat flux
over the BKS (Additional file 5: Fig. S5d and S5h).
Actually, the warmer and wetter air from the Northern Atlantic tends to weaken sea-air temperature difference and humidity difference. As a result, it could
mitigate turbulent heat flux from the ocean to the air.

3.4 Relationship between internal atmospheric variability
and the WACE patternFigures 4 and 5 suggest that UB-like anticyclonic circulation and the NAO have essential impacts on theformation of the WACE pattern. This subsection quantitatively investigates the relationships among UB, theNAO, and the WACE pattern. The relationships are
summarized in Fig. 6 based on the simulated 4100
years. The WACE index tends to be higher when boththe UB index and NAO index are large (Fig. 6b). This
result indicates that UB, together with the positive
NAO, contributes substantially to the amplification of
the WACE pattern, supporting the claim by Luo et al.
(2016b).
The winter SAT over the BKS varies similarly to the
WACE index in accordance with changes in winter UB
and NAO (Fig. 6c), indicating that the WACE index
largely reflects the variation of winter SAT over the
BKS, as suggested by Komatsu et al. (2022). The winterSAT over the BKS tends to be warmer when both the
UB index and NAO index are large (Fig. 6c). This is presumably because the circulation condition provides anefficient energy pathway from the North Atlantic to the
BKS (Fig. 5; Luo et al. 2017). The quantitative analysis andregression analysis of the large simulation cases indicate
similar contributions of the UB and positive NAO on theformation of warmer SAT over the BKS (Figs. 6c, Additional file 6: S6b and S6d).

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

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Fig. 6 Relationship between winter internal atmospheric variability and the WACE pattern for the simulated 4100 years. Scatterplot of the winterNAO and UB indexes. Values denote the year count (a), averaged WACE index (b), and standardized averaged winter SAT over the BKS (c) and CEU(d)Winter SAT over CEU tends to be colder under the
combination of UB and negative NAO (Fig. 6d). The
effect of winter UB on cold CEU is more significant thannegative NAO (Figs. 6d, Additional file 6: S6b, and S6d),
probably because the negative NAO mainly decreases
the temperature over northern Eurasia rather than CEU(Additional file 6: Fig. S6d). Although the observation
results could reflect the relationships among UB, theNAO, and the WACE pattern, which are mainly similar
to those large-ensemble show, the number of observedcases is limited to draw a robust conclusion (Additional
file 6: Figs. S6a, S6c, and Additional file 7: S7).

4 Discussion
Some studies claimed the surface warming caused by sea
ice loss over the BKS provides conditions favorable forthe occurrence of UB (Luo et al. 2016a; Kim et al. 2022),
which in turn amplifies local sea ice loss (Gong and Luo
2017; Tyrlis et al. 2020). Our study also investigated therelationship between winter SIC over the BKS and theZhou et al. Progress in Earth and Planetary Science(2023) 10:59

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Fig. 7 Relationship between winter sea ice over the BKS and winter internal atmospheric variability. a Scatterplot for standardized winter SIC
over the BKS and winter UB index for the observed 41 years (blue squares) and simulated 4100 years (grey dots). b Similar to a, but for the
relationship between standardized winter SIC over the BKS and winter NAO index. The texts denote the correlation coefficient and p-value

winter UB frequency based on the large-ensemble historical experiment. Their correlation is not statisticallysignificant in both observation and simulation (Fig. 7a),suggesting the influence of winter sea ice decline on
winter UB frequency is weak. The robust two-way interaction between the sea ice loss and UB frequency at different timescales could be a future research topic. Early
winter and late autumn sea ice loss tends to weaken thestratospheric polar vortex, leading to more frequent negative NAO state in winter (Kim et al. 2014; Nakamura
et al. 2015). Results in this study suggest the negative
correlation between winter sea ice over the BKS and winter NAO is very weak although positive NAO is slightly
favored when sea ice declines (Fig. 7b).
Figure 8 presents the winter SAT and SLP anomalies related to winter sea ice decline over the BKS. The
sea ice-induced atmospheric variability could be represented by the 100-member ensemble-mean, which
establishes WACE-like temperature contrast between the
BKS and CEU. In the high-correlation group, the internal atmospheric variability (Ural blocking and positive
phase of NAO, Fig. 4b) possibly produces a warmer BKS
and colder CEU than the ensemble-mean, leading to agreater WACE-like pattern (Fig. 8). On the other hand,in the low-correlation group, the internal atmosphericFig. 8 Winter SAT and SLP anomalies related to winter sea
ice decline over the BKS. Regressed winter SAT anomalies (°C)
over the BKS (70°–80° N, 30°–70° E) and CEU (40°–60° N, 60°–120° E),
and SLP anomalies (hPa) over the Ural region (40°–80° N, 40°–80° E)
on reversed and normalized winter SIC over the BKS. The blackstars and grey boxes denote the observation results and simulation
results for each member, respectively. The upper and lower whiskersof the box are the maximum and minimum values, respectively;
the upper and lower sides of the box are the upper and lower
quartiles, respectively. Horizontal solid lines across the boxes
denote the 100-member ensemble-mean. The green and orange
dots denote the 10-member mean of the high-correlation groupand 10-member mean of the low-correlation group, respectively

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

variability (negative SLP anomalies over the Ural region,
Fig. 4c) acts oppositely to what happens in the high-correlation group. Hence, the atmospheric response to sea
ice is obscured (Fig. 8). Overall, the internal atmosphericvariability in the high-correlation group and low-correlation group both affects the WACE pattern, but their roles
are opposite.
Finally, we will discuss the limitation of the current
study. The large-ensemble historical simulation adopted
in this study was created using the AGCM. As in Fig. 8,
the model is likely to underestimate the SAT anomaly
over the BKS related to local sea ice decline. Despite that,the observed SLP anomaly over the Ural region and SAT
anomaly over CEU are within the range of the ensemble
spread (Fig. 8). These results imply that the model has
the ability to simulate SLP anomaly over the Ural region
and SAT anomaly over CEU as they are observed. However, there is the possibility that the underestimated SAT
anomalies over the BKS can indirectly cause underestimation of the cold CEU. Experiments with two-way
atmosphere–ocean coupling configuration will be suitable for the investigation of sea ice impact. Large-ensemble experiment conducted by the atmosphere–ocean
general circulation model is desired for further investigation of the WACE pattern.

5 Conclusion
This study analyzes a large-ensemble historical experiment to investigate the roles of internal atmospheric variability in the observed interannual variation of the WACE
pattern. Analysis of the observed and simulated winterSAT change over the BKS indicates that the warming
trend over the BKS is mainly caused by external forcing,but the contribution of internal atmospheric variability
cannot be ignored (Figs. 1 and 2). On the contrary, thewinter cooling trend over CEU is largely regulated by theinternal atmospheric variability (Figs. 1 and 2).
Results of EOF analysis on winter SAT anomalies overEurasia suggest that both external forcing and internal
atmospheric variability contribute to the observed interannual variation of the WACE pattern (Fig. 3b). Comparison of ensemble members suggests UB-like anticyclonic
circulation and the NAO are the key internal atmosphericvariability responsible for the observed interannual variation of the WACE pattern (Fig. 4). The UB-like anticyclonic circulation, together with the positive NAO phase,provides an efficient energy transport pathway from theNorth Atlantic to the BKS, promoting warm advection
and increasing regional winter SAT over the BKS (Fig. 5).
The northerly wind that dominates to the east of the Ural
region enhances cold advection and reduces regional
winter SAT over CEU (Fig. 5).

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This study also quantitatively analyzes the combined
effect of UB and NAO on the WACE pattern using alarge-ensemble experiment. Results indicate similar contributions of the UB and positive NAO on the formation of warmer SAT over the BKS (Fig. 6c). The effect ofwinter UB to form colder CEU is greater than that of thenegative NAO (Fig. 6d).
Other factors, such as winter tropical Pacific coolingand autumn snow cover reduction, also contribute to cold
winters in CEU (Matsumura and Kosaka 2019; Luo et al.
2021; Xu et al. 2018). Recent changes in the Arctic Ocean
have altered the hydrological cycle in circumpolar terrestrial regions (Sato et al. 2022). Hence, it is necessary to
study the variability in land–atmosphere interaction over
CEU in the future. Hori and Oshima (2018) compared
the WACE pattern using large-ensemble experiments
under historical simulation and non-warming simulation.
Considering the possible interaction between sea ice loss
and internal atmospheric variability, future studies could
utilize large-ensemble experiments to investigate thesensitivity of the future WACE pattern under continuing
reduction of Arctic sea ice.
This study focuses on the seasonal mean SAT pattern. The follow-on studies are expected to conduct theanalysis of daily to subseasonal timescale to understandthe development of the WACE pattern as such aspectdrew attention in recent studies (Tyrlis et al. 2020; Kimet al. 2021; Yin et al. 2023). The subseasonal analysis forthe development of internal atmospheric variability andtemperature anomalies over the key regions using largeensemble experiments could be a future research topic.
Abbreviations
AGCM	Atmospheric general circulation model
BKS	Barents–Kara seas
CEU	Central Eurasia
COBE	Centennial in situ Observation‐Based Estimates
EOF	Empirical orthogonal function
HIST	Historical experimentHC	High-correlation
LC	Low-correlation
NAO	North Atlantic Oscillation
PC	Principal component
SAT	Surface air temperature
SIC	Sea ice concentration
SLP	Sea-level pressure
SST	Sea surface temperature
UB	Ural blocking
WACE	Warm Arctic–Cold Eurasia

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​023-​00591-x.
Additional file 1. Figure S1. Two leading modes governing the wintertemperature over Eurasia.
Additional file 2. Figure S2. Winter NAO index and UB frequency.

Zhou et al. Progress in Earth and Planetary Science(2023) 10:59

Additional file 3. Figure S3. Winter temperature change over the BKS
and CEU in selected two groups.
Additional file 4. Figure S4. Winter meridional and zonal temperatureadvection pattern related to reversed winter sea ice change over the BKS.
Additional file 5. Figure S5. Winter sensible and latent heat flux patternrelated to reversed winter sea ice change over the BKS.
Additional file 6. Figure S6. Winter SAT pattern related to internal atmospheric variability change.
Additional file 7. Figure S7. Relationships among winter UB, the NAO,
and the WACE pattern for the observed 41 years.
Acknowledgements
We thank James Buxton MSc, from Edanz (https://​jp.​edanz.​com/​ac), for editinga draft of this manuscript. We also appreciate the editor and two anonymous
reviewers for their constructive comments.
Author contributions
XZ proposed the topic, analyzed the data, and wrote the original manuscript.
TS and LS advised the analysis and helped in the interpretation of the results
and the revision of the manuscript.
Funding
This study was supported by the Japan Society for the Promotion of Science
KAKENHI (Grant Number: JP19H05668), the Arctic Challenge for Sustainability
II (ArCSII) project (Grant Number: JPMXD1420318865), the SENTAN program(JPMXD0722680734), and the JST SPRING (Grant Number: JPMJSP2119), Japan.
Availability of data and materials
The atmosphere data from European Centre for Medium-Range Weather
Forecasts Reanalysis v5 are available at https://​cds.​clima​te.​coper​nicus.​eu/​
cdsapp#​!/​search?​type=​datas​et. The SST and SIC data from Centennial Observation‐Based Estimates of SST Version 2 are available at https://​psl.​noaa.​gov/​
data/​gridd​ed/​data.​cobe2.​html. The historical simulation data from Databasefor Policy Decision-Making for Future Climate Change are available at http://​d4pdf.​diasjp.​net/​d4PDF.​cgi.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 28 April 2023 Accepted: 16 September 2023

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