Influence of autumn Kara Sea ice on the subsequent winter minimum temperature over the Northeast China

Abstract

Many previous studies have examined the influence of Arctic sea ice on the weather/climate of the Northern Hemisphere. However, the precursor signals of Arctic sea ice regarding East Asian winter low temperatures, which are important for climate prediction, have received little attention. This study identified an out‐of‐phase relationship between the autumn sea ice area of the Kara Sea (SICK) and subsequent winter minimum temperature in Northeast China (NEC) during 1979–2019, that is, diminished SICK facilitates cooling anomalies at NEC. Further results showed that the Arctic Oscillation (AO) acts as a bridge in the linkage between the autumn SICK and minimum temperature in NEC. Diminished autumn SICK leads to a weakened polar vortex in both the troposphere and the stratosphere in the subsequent winter through vertical propagation of planetary waves, contributing to a negative phase of the AO. Accordingly, the SICK is followed by anomalous Rossby wave train that originates from Mediterranean Sea and propagates eastward to Northeast Asia. Thus, the SICK has substantially influences on the Mongolia cyclone and minimum temperature in NEC by modulation of the AO. Moreover, the shrinking SICK could lead to the upper‐level decelerated westerly anomalies at East Asia through altering the equator‐to‐pole temperature gradient and induce negative minimum temperature anomalies at NEC. The results of this study have importance regarding the prediction of low temperature anomalies over NEC.

Full text

RESEARCH ARTICLE
Influence of autumn Kara Sea ice on the subsequent winter
minimum temperature over the Northeast China
Tingting Han
1,2,3
| Xin Zhou
1
| Shangfeng Li
4
| Botao Zhou
1,2,3
1
School of Atmospheric Sciences, Nanjing
University of Information Science and
Technology, Nanjing, China
2
Collaborative Innovation Center on
Forecast and Evaluation of Meteorological
Disasters/Key Laboratory of
Meteorological Disaster, Ministry of
Education, Nanjing University of
Information Science and Technology,
Nanjing, China
3
Nansen-Zhu International Research
Centre, Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing,
China
4
Jilin Provincial Key Laboratory of
Changbai Mountain Meteorology &
Climate Change, Laboratory of Research
for Middle-High Latitude Circulation
Systems and East Asian Monsoon,
Institute of Meteorological Sciences of
Jilin Province, Changchun, China
Correspondence
Shangfeng Li, Jilin Provincial Key
Laboratory of Changbai Mountain
Meteorology & Climate Change,
Laboratory of Research for Middle-High
Latitude Circulation Systems and East
Asian Monsoon, Institute of
Meteorological Sciences of Jilin Province,
Changchun, China.
Email: ice-lsf@163.com
Funding information
Guangdong Major Project of Basic and
Applied Basic Research, Grant/Award
Number: 2020B0301030004; National
Natural Science Foundation of China,
Grant/Award Number: 41875119; Science
and Technology Development Plan in Jilin
Province of China, Grant/Award Number:
20230203135SF
Abstract
Many previous studies have examined the influence of Arctic sea ice on the
weather/climate of the Northern Hemisphere. However, the precursor signals
of Arctic sea ice regarding East Asian winter low temperatures, which are
important for climate prediction, have received little attention. This study
identified an out-of-phase relationship between the autumn sea ice area of the
Kara Sea (SICK) and subsequent winter minimum temperature in Northeast
China (NEC) during 1979–2019, that is, diminished SICK facilitates cooling
anomalies at NEC. Further results showed that the Arctic Oscillation
(AO) acts as a bridge in the linkage between the autumn SICK and minimum
temperature in NEC. Diminished autumn SICK leads to a weakened polar vor-
tex in both the troposphere and the stratosphere in the subsequent winter
through vertical propagation of planetary waves, contributing to a negative
phase of the AO. Accordingly, the SICK is followed by anomalous Rossby wave
train that originates from Mediterranean Sea and propagates eastward to
Northeast Asia. Thus, the SICK has substantially influences on the Mongolia
cyclone and minimum temperature in NEC by modulation of the
AO. Moreover, the shrinking SICK could lead to the upper-level decelerated
westerly anomalies at East Asia through altering the equator-to-pole tempera-
ture gradient and induce negative minimum temperature anomalies at NEC.
The results of this study have importance regarding the prediction of low tem-
perature anomalies over NEC.
KEYWORDS
AO, Rossby wave, sea ice at Kara Sea, winter low temperature at Northeast China
Received: 13 July 2023 Revised: 31 March 2024 Accepted: 3 April 2024
DOI: 10.1002/joc.8461
Int J Climatol. 2024;1–13. wileyonlinelibrary.com/journal/joc © 2024 Royal Meteorological Society 1

1|INTRODUCTION
Arctic sea ice, which is an important component of
Earth's climate system, has substantial influences on
global climate variation/variability through both surface
albedo modulation and sea–ice–air interactions (Gao
et al., 2015; Screen et al., 2014; Vihma, 2014). The Arctic
is warming at a rate approximately four times faster than
the global average, which is accelerating the melting of
sea ice (Rantanen et al., 2022). Variations in Arctic sea
ice, especially diminishment in concentration and reduc-
tion in thickness, can cause atmospheric circulation
anomalies in the Northern Hemisphere and even globally
(Kwok & Rothrock, 2009; Perovich & Richter-
Menge, 2009; Screen & Simmonds, 2010).
Mounting evidences have documented the influ-
ences of Arctic Sea ice on local and global climate/
weather conditions (Honda et al., 2009;Mori
et al., 2014; Zhang et al., 2022). For example, variations
in Arctic sea ice can alter the turbulent heat flux
between the ocean and atmosphere and modulate atmo-
spheric circulation anomalies in the Northern Hemi-
sphere (Li et al., 2015;Vihma,2014;Wang&Liu,2016).
Additionally, Arctic Sea ice anomalies affect air temper-
ature anomalies and the intensity/duration of the block-
ings at the lower troposphere (Cohen et al., 2014;Luo
et al., 2019;Maetal.,2022).Theseaiceanomaliesin
Greenland-Barents-Okhotsk Sea cause local tempera-
ture anomalies through the diabatic heating process and
further influence zonal wind in Ural by advection and
convection process, thus resulting in Ural block forma-
tion (Ma et al., 2022). In addition, sea ice loss anomalies
in western Greenland could impact Greenland Block-
ings and extreme cold weather at the mid-eastern
United States (Chen & Luo, 2021). Moreover, the dimin-
ishment of Arctic sea ice anomalies could persist from
autumn to early winter and lead to the enhancement of
upward propagation planetary wave, causing a weak
stratospheric polar vortex and shift toward Eurasia (Kim
et al., 2014; Zhang et al., 2016). The weak stratospheric
polar vortex contributes to the negative phase of Arctic
Oscillation (AO)/North Atlantic Oscillation (NAO) via
downward propagation of planetary wave in the late
winter, favouring cooling winter at the Eurasia (Hoshi
et al., 2019;Kretschmeretal.,2020;Wu&Smith,2016;
Zhang et al., 2018). However, some studies have pro-
posed that the shrinking sea ice anomalies are related to
a positive phase of the AO (Screen et al., 2014;Strey
et al., 2010). Therefore, the role of AO in the relation-
ship of Arctic Sea ice and Eurasian climate required
more efforts.
Ample attention has been directed toward the
influences of sea ice anomalies in the Barents-Kara
Sea (BKS) on Eurasian climate anomalies. The occur-
rences of Eurasian cold events are closely related to
the reduction in sea ice anomalies in the BKS (Luo
et al., 2017;Screen&Simmonds,2010;Yang
et al., 2020). Moreover, precipitation anomalies and
summer droughts in China can be attributed in part to
changes in Arctic Sea ice extent (He et al., 2018;Li,
Chen, et al., 2018; Li, Jiang, et al., 2018;Shen
et al., 2019;Zhouetal.,2023). For example, Wu et al.
(2009) suggested that the decrement in spring sea ice
anomalies at the BKS is favourable for increased sum-
mer precipitation anomalies in central China by
anomalous wave activity at northern Europe. He et al.
(2018) noted that anomalous sea ice in the Barents
SeainJuneexertssubstantialimpactsontheEast
Asian triple rainfall pattern during August by Silk
Road pattern and Pacific–Japan pattern. Recently,
Sun et al. (2022) demonstrated the prediction value of
winter sea ice anomalies over the BKS for spring
extreme heat events at Eurasia. The issue that the pre-
ceding influences of sea ice at the BKS on extreme
events at China and the associated mechanisms is of
great importance for climate prediction.
Northeast China (NEC) is located in the mid- to
high-latitudes of Eurasia, where climate is highly sensi-
tive to global warming. Considerable efforts have been
devoted to investigation of the atmospheric regimes
associated with winter temperature anomalies in NEC,
including the polar vortex (Kim et al., 2014), Siberian
High (Ding & Krishnamurti, 1987;Li,Chen,
et al., 2018; Li, Jiang, et al., 2018), and Ural blocking
(Luo et al., 2016; Yao et al., 2017). Dai et al. (2019)
revealed substantial influences of sea ice anomalies
over the BKS on winter air temperature in NEC.
Recently, the connection between previous Arctic sea
ice anomalies and summer precipitation at NEC has
been demonstrated (Han et al., 2021,2023). During
recent decades, extreme low-temperature events
(ELTEs) in NEC have occurred with increasing fre-
quency (Li & Wang, 2012;Qiaoetal.,2014;Yang
et al., 2020). However, the relationship between Arctic
seaice(especiallyBKSseaiceinprecedingmonths)
and low temperatures in winter in NEC, which is the
focus of this study, has received little attention. The
role of AO in the relationship between Arctic sea ice
and low temperatures at NEC is also explored.
The remainder of this paper is organized as follows.
Section 2introduces the datasets and methods used in
this study. Section 3presents details of the relationship
between Arctic sea ice and minimum temperatures in
NEC, together with a description of the possible control-
ling mechanisms. Finally, a brief conclusion and discus-
sion are presented in Section 4.
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2|MATERIALS AND METHODS
In the present study, an advanced daily mean air temper-
ature and monthly minimum temperature observation
dataset (i.e., CN05.1) is used for 1961–2020, with a hori-
zontal resolution of 0.25×0.25(Wu & Gao, 2013). This
dataset has been constructed by interpolating data from
over 2400 meteorological stations over China. Another
monthly minimum temperature data was obtained from
the Climatic Research Unit gridded Time Series (CRU TS
V4.07) during 1901–2022, with a 0.5×0.5horizontal
resolution (Harris et al., 2020).
The monthly atmospheric reanalysis dataset is
derived from the National Centre for Environment Pre-
diction/National Centre for Atmospheric Research for
1979–2020, on a 2.5×2.5latitude-longitude grid
(Kalnay et al., 1996). Variables used in this study include
sea level pressure (SLP), geopotential height, horizontal
wind, and vertical motion. The monthly surface sensible
heat flux and surface latent heat flux data come from the
fifth-generation ECMWF reanalysis dataset, with a hori-
zontal resolution of 1×1(Hersbach et al., 2023). The
monthly sea ice concentration dataset for 1870–2020,
with a 1×1grid, stem from the Met Office Hadley
Centre (Rayner et al., 2003). Sea ice area is obtained via
multiplying sea ice concentration by grid area. The AO
index comes from the Climate Prediction Centre: https://
www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_
ao_index/ao.shtml.
Considering the utilization of satellite data after 1979,
the common time period spans 1979–2020, and winter
refers to December in the current year to February in the
subsequent year. Autumn refers to October and
November. NEC is defined as the region north of 35N
and east of 115E within China. A minimum tempera-
ture index (Tmin_NEC) is defined as the normalized
area-weighted average of winter minimum temperature
in NEC. Additionally, ELTEs are determined based on
the following two conditions: (1) daily mean air tempera-
ture is lower than or equal to the threshold value of low
temperature; and (2) more than 50% of the grid points in
NEC satisfy condition (1) concurrently (Li, Chen,
et al., 2018; Li, Jiang, et al., 2018). The threshold value of
low temperature is defined as the 10th percentile value
of the daily mean temperature data during 1991–2020. It
is notable that the ELTEs and Tmin_NEC indices exhibit
high covariance for the period 1961–2019 (Figure 1c),
with a correlation coefficient of −0.76 (above the 99%
confidence level).
Composite, regression and correlation analyses are
performed to investigate the atmospheric circulation
anomalies associated with the Tmin_NEC and sea ice.
FIGURE 1 (a) Temporal
evolutions of original (black
line) and detrended (red line)
winter extreme low-temperature
events (ELTEs) in Northeast
China (NEC) during 1961–2019.
(b) Regression map of winter
minimum temperatures (C) in
NEC with reference to the
ELTEs index. Stippled (hatched)
areas indicate values that
significantly exceed the 95%
(90%) confidence level,
estimated using the Student's
t-test. A minimum temperature
index (Tmin_NEC) is defined as
the normalized area-weighted
average of winter minimum
temperature over NEC.
(c) Scatter plot of Tmin_NEC
against the ELTEs index during
1961–2019. [Colour figure can be
viewed at
wileyonlinelibrary.com]
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The student's t-test is used to determine statistical signifi-
cance. Moreover, the linear trend has been removed from
all the variables before analysis to isolate the interannual
variation.
3|RESULTS
3.1 |Relationship between autumn
Arctic sea ice and winter low temperatures
in Northeast China
Figure 1a depicts the temporal evolution of the fre-
quency of ELTEs in NEC during the past six decades.
TheoccurrenceofELTEswasclearlyonadownward
trend before the 1990s, with minimum frequencies
during the 1990s. The reduction in ELTEs during the
1990s can be attributed to the smaller number of
ELTEs that occurred during January and February in
that decade (Figure S1), consistent with the findings
of Li, Chen, et al. (2018),Li,Jiang,etal.(2018). In the
past six decades, ELTEs occurred 157 times during
February, accounting for approximately 45.2% of the
total number of winter ELTEs, 101 times in January
(accounting for 29.1% of the total), and 89 times in
December (accounting for 25.6% of the total;
Figure S1). A positive ELTEs index is accompanied by
significantly cooling minimum temperature anoma-
lies throughout the NEC, along with large values in
northern parts (Figure 1b).
As implied by previous studies on the relationship
between autumn Arctic sea ice and winter air tempera-
ture at East Asia (Li et al., 2015; Sun et al., 2022), the cor-
relation coefficients between autumn Arctic sea ice and
ELTEs in NEC were determined to identify the region of
interest (Figure 2a). Figure 2a clearly shows that signifi-
cant negative correlations occupy the Kara Sea and the
north. To facilitate analysis, a sea ice index is defined as
the normalized area-weighted average of autumn sea ice
area in the Kara Sea (77
–83N, 27
–83E; hereafter, the
SICK index). Significant out-of-phase variations are
observed between the SICK and Tmin_NEC indices, with
a correlation coefficient of −0.47 for the period 1979–
2019 (above the 99% confidence level; Figure 2d). To ver-
ify the interannual relationship between the SICK index
and low-temperature anomalies in NEC, Figure 2b illus-
trates the spatial distribution of the minimum tempera-
ture anomalies over NEC associated with the SICK index,
based on the CN05.1 dataset. When sea ice anomalies
diminish over the Kara Sea during autumn, profound
negative minimum temperature anomalies dominate
NEC during the following winter. Consistent results are
FIGURE 2 (a) Correlation
map between preceding autumn
Arctic Sea ice area and the
ELTEs index during 1979–2019.
A sea ice index (SICK) is defined
as the normalized area-weighted
average of preceding autumn sea
ice area over the Kara Sea and
its north (77
–83N, 27
–83E).
(b) Regression map of winter
minimum temperature in NEC
(C; based on the CN05.1
dataset) with reference to the
negative SICK index during
1979–2019. Stippled (crossed)
areas indicate values that
significantly exceed the 95%
(90%) confidence level,
estimated using the Student's
t-test. (c) Same as (b), but the
minimum temperature (C) is
based on CRU TS V4.07 dataset.
(d) Scatter plot of the autumn
SICK index and the Tmin_NEC
index during 1979–2019. [Colour
figure can be viewed at
wileyonlinelibrary.com]
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observed by the monthly minimum temperature data
from CRU (Figure 2c).
The features of the atmospheric circulation anomalies
with respect to the ELTEs index are shown in
Figure 3a,c. The ELTEs-related circulation anomalies dis-
play a barotropic structure over the Eurasian continent.
Specifically, ELTEs are characterized by positive sea level
pressure (SLP) and geopotential height anomalies over
the Arctic, and negative values over mid-latitude regions
of the North Atlantic and East Asia. Concurrently, anti-
cyclonic and cyclonic wind anomalies occupy the Arctic
and the North Atlantic/East Asia, respectively. The
meridional seesaw pattern of SLP anomalies over
the mid- to high-latitudes of the North Atlantic resembles
a negative phase of the AO. Additionally, the circulation
anomalies exhibit a zonally oriented wave train in the
mid-latitudes in the middle and upper troposphere,
together with dominance of positive–negative–positive–
negative height anomalies over the Northwest Atlantic,
Northeast Atlantic, Central Asia, and East Asia, respec-
tively. The above results indicate that the negative phase
of the AO and the cyclone centred over Mongolia contrib-
ute to cold anomalies in NEC during winter.
As presented in Figure 3b,d, following decreased sea
ice anomalies during autumn at the Kara Sea, prominent
positive SLP and geopotential height anomalies prevail
over the Arctic and negative anomalies dominate the
mid-latitudes of the North Atlantic and Eurasian in
the following winter, accompanied by an abnormal anti-
cyclone over the Arctic and a cyclone centred in the mid-
latitude North Atlantic and Eurasian. It suggests that
anomalous reduction in sea ice in the Kara Sea for
autumn is followed by a negative phase of the AO during
winter, which contributes to cooling temperature anoma-
lies in NEC (Figure 2b). These results confirm that the
autumn SICK index has a profound influence on
the minimum temperature in NEC in the following
winter.
3.2 |Possible mechanism
Observational analysis revealed a negative spectrum of
AO responses to sea ice reduction in the Kara Sea
(Figure 4a), with a correlation coefficient of 0.47 for the
period 1979–2019 (above the 99% confidence level).
The AO highly covaries with Tmin_NEC during the past
four decades, with a correlation coefficient of 0.53 (above
the 99% confidence level).
A positive AO index is in accord with significant neg-
ative height anomalies and cyclonic circulation over the
Arctic. And prominent positive height anomalies occur
over the East Asia, North Atlantic and North Pacific,
along with three respective anomalous anticyclones
centred at Mongolia, the North Atlantic and Aleutian
(Figure 5a,b). Meanwhile, decelerated westerly jet anom-
alies appear over northern China (Figure 5c). The abnor-
mal easterly on the southern flank of the Mongolian
anticyclone favours transportation of warming flow from
the Northwest Pacific toward NEC. Those conditions
jointly lead to dominance of warming temperature anom-
alies in NEC (Figure 5d).
FIGURE 3 Linear regression of (a, b) sea level pressure (SLP, contours; mb) and 850-hPa horizontal wind (vectors; m s
−1
), and (c, d)
500-hPa geopotential height (contours; m) and horizontal wind (vectors; m s
−1
) against the (left) ELTEs and (right) negative SICK indices
during 1979–2019. Dark (light) shading indicates values that exceed significantly exceed the 95% (90%) confidence level, estimated using the
Student's t-test. [Colour figure can be viewed at wileyonlinelibrary.com]
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FIGURE 4 Scatter plots of
(a) the autumn SICK index and
the winter AO index, and (b) the
winter AO index and the
Tmin_NEC index during 1979–
2019. [Colour figure can be
viewed at
wileyonlinelibrary.com]
FIGURE 5 Linear regression pattern of winter (a) SLP (contours; mb) and 850-hPa horizontal wind (vectors; m s
−1
), (b) 500-hPa
horizontal wind (vectors; m s
−1
) and geopotential height (contours; m), (c) 300-hPa zonal wind (m s
−1
) and (d) minimum temperature (C)
over NEC against the winter AO index during 1979–2019. Dark (light) shading indicates SLP anomalies in (a), geopotential height anomalies
in (b) and zonal wind anomalies in (c) that significantly exceed the 95% (90%) confidence level, estimated using the Student's t-test. Stippled
areas in (d) indicate values that significantly exceed the 95% confidence level, estimated using the Student's t-test. [Colour figure can be
viewed at wileyonlinelibrary.com]
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As shown in Figure 3, the cyclone centred Mongolia
exerts substantial effects on minimum temperature
anomalies at NEC. To facilitate analysis, a Mongolia
cyclone index (MCI) is defined as averaged vorticity at
500 hPa over Mongolia (43
–60N, 80
–120E; as shown
by the red rectangle in Figure 6a). A positive MCI index
implies a strong cyclone centring Mongolia. Anomalous
Mongolia cyclone facilitates significant cooling tempera-
ture anomalies over NEC (Figure 6b), with a correlation
coefficient −0.75 between MCI and Tmin_NEC indices
(above 99% confidence level). The correlation coefficient
between the autumn SICK (winter AO) and MCI is −0.41
(−0.47) during 1979–2019 (both above 99% confidence
level). It is notable that when the linear influence of AO
was eliminated from the autumn SICK, the relation
between the autumn SIC and MCI weakened, with a cor-
relation coefficient −0.22 (insignificant). It suggests that
the autumn SICK exerts an impact on the Mongolia
cyclone and minimum temperature anomalies at NEC
during winter through modulation of winter AO.
Previous studies revealed that the Arctic sea ice can
influence the atmospheric circulation and climate at East
Asia via the propagation of Rossby waves (Fan
et al., 2018; Li et al., 2019). To investigate the potential
influence of the SICK on Rossby wave trains, we explored
the anomalous divergence wind and Rossby wave source
(RWS) in association with the SICK (Figure 7a,b). The
autumn SICK anomalies are followed by the dominance
of upper-level divergence and RWS anomalies and near-
surface convergent anomalies over the Mediterranean
Sea, which is consistent with the RWS anomalies related
to the AO (Figure 7d,e). The advection of vorticity by
divergence wind anomalies can act as an effective Rossby
wave source (Sardeshmukh & Hoskins, 1988). Accord-
ingly, the linear regression patterns of the 300-hPa wave
activity flux (vectors) and meridional wind (shading) in
FIGURE 6 (a) Linear regression pattern of winter 500-hPa vorticity (10
−6
s
−1
) against the Tmin_NEC index during 1979–2019. Dark
(light) shading indicates values that significantly exceed the 95% (90%) confidence level, estimated using the Student's t-test. A winter
Mongolia cyclone index (MCI) is defined as the normalized area-weighted average of winter vorticity over Mongolia (43
–60N, 80
–120E;
shown by the red rectangle). (b) Regression map of winter minimum temperature (C) in NEC with reference to the MCI index during 1979–
2019. Stippled (crossed) areas indicate values that significantly exceed the 95% (90%) confidence level, estimated using the Student's t-test.
(c) Scatter plot of the MCI index and the Tmin_NEC index during 1979–2019. (d) Scatter plot of the winter AO index and the MCI index
during 1979–2019. [Colour figure can be viewed at wileyonlinelibrary.com]
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relation to the SICK are further investigated (Figure 7c).
The wave activity flux, computed based on the formula-
tion of Plumb (1985), can depict the propagation of sta-
tionary Rossby waves. Associated with decreased sea ice
anomalies at the Kara Sea, an abnormal Rossby wave
train originates over the Mediterranean and propagates
eastward to Northeast Asia via a polar route at mid- to-
high-latitudes, by which the SICK influences East Asian
circulations and winter temperature anomalies at the
NEC. Concurrently, alternation of profound southerly
and northerly anomalies prevails over the mid- to- high-
latitudes stretching from the Mediterranean to Northeast
Asia. Similar wave propagation could be recognized in
the AO-related WAF (Figure 7f). The AO-related meridi-
onal wind anomalies exhibit alternative occurrence of
northerly and southerly at the mid- to- high-latitudes of
Eurasia. These results suggest that the AO acts a bridge
in the linkage between the autumn SICK and winter
Tmin_NEC.
Furthermore, reduction in the SICK favours increased
area of opened waters and absorption of solar radiation,
leading to anomalous release of turbulent heat flux
(including sensible and latent heat flux) from the surface
to the overlying atmosphere (Figure 8a,b). Hence, warm
air temperature anomalies prevail and lead to vertical
ascent (Figure 8c,d). Some studies found that Arctic Sea
ice loss weakens the stratospheric polar vortex during
winter through upper propagation of planetary waves
and then induces a negative phase of the AO/NAO
(Cohen et al., 2014; Kim et al., 2014). In association with
FIGURE 7 Linear regression pattern of winter (a) SLP (contours; mb), 850-hPa divergent wind (vectors; m s
−1
) and Rossby wave source
(shadings; 10
−11
s
−2
), (b) 300-hPa geopotential height (contours; m), divergent wind (vectors; m s
−1
) and Rossby wave source (shadings;
10
−11
s
−2
), and (c) 300-hPa meridional wind (shadings; m s
−1
) and wave activity flux (vectors; m
2
s
−2
) against the negative autumn SICK
index during 1979–2019. Stippled areas in (a) and (b) indicate Rossby wave source anomalies that significantly exceed the 95% confidence
level, estimated using the Student's t-test. Stippled areas in (c) indicate meridional wind anomalies that significantly exceed the 90%
confidence level, estimated using the Student's t-test. (d–f) Same as (a–c), but against the winter AO index. [Colour figure can be viewed at
wileyonlinelibrary.com]
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decreased sea ice anomalies in the Kara Sea, remarkable
positive geopotential height anomalies over the Arctic
extend from surface to the stratosphere in autumn, which
could be maintained to the following winter (Figure 9). It
suggests that a diminished (augmented) SICK is followed
by a weakened (intensified) polar vortex in the tropo-
sphere and stratosphere during the subsequent winter
(Figure 3). The connection between the SICK index and
the polar vortex might be related to vertical wave activity
(Christiansen, 2001; He et al., 2019). To examine the dif-
ference in quasi-stationary planetary wave anomalies,
years with a positive (negative) SICK index are character-
ized by values greater (less) than 0.5 (−0.5) (Table 1).
Figure 10 presents the zonal wind (blue contours), and
the cross section of Eliassen–Palm (EP) flux (vectors)
and its divergence (red contours) in association with the
SICK index during autumn and the following winter.
During autumn, apparent abnormal EP fluxes related to
the SICK index propagate from the lower troposphere
upward into the upper stratosphere (20 hPa) in the
mid- to high-latitudes and then bend equatorward
(Figure 10a; vectors). During the following winter, there
are significant downward EP fluxes in the stratosphere
(50 hPa) north of 70N (Figure 10b; vectors), implying
downward propagation of planetary waves from the
stratosphere toward the surface. Anomalous divergence
of the EP flux emerges at high latitudes in the lower
stratosphere (Figure 10b; red contours), which accelerate
anomalous westerlies at high latitudes. Therefore, promi-
nent positive westerly anomalies appear at 50
–80N
(Figure 10b; blue contours). These results provide evi-
dence that sea ice loss in the Kara Sea can cause vertical
propagation of planetary waves into the stratosphere,
weakening the stratospheric polar vortex, and contribut-
ing to a negative phase of the AO and cold minimum
temperature anomalies in NEC.
In addition, increased sea ice anomalies could induce
cooling air temperature anomalies due to high albedo of
FIGURE 8 Linear regression pattern of preceding autumn (a) surface sensible heat flux (W m
−2
), (b) surface latent heat flux (W m
−2
),
(c) 850-hPa vertical motion (10
−2
Pa s
−1
) and (d) 850-hPa air temperature (C) against the negative SICK index during 1979–2019. Stippled
(crossed) areas indicate values that significantly exceed the 95% (90%) confidence level, estimated using the Student's t-test. [Colour figure
can be viewed at wileyonlinelibrary.com]
HAN ET AL.9
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FIGURE 9 Zonal distribution of geopotential height (m) anomalies along the section of 70
–90N during (a) autumn and (b) winter
regressed on the negative SICK index during 1979–2019. Dark (light) shading indicates values that significantly exceed the 95% (90%)
confidence level, estimated using the Student's t-test.
TABLE 1 Years characterized by positive/negative values of the SICK index during 1979–2019.
Positive autumn SICK Negative autumn SICK
Years 1982,1987,1988,1989,1991,1992,1993,1997,1998,
2002,2003,2004,2014,2019
1979,1981,1983,1984,1985,2000,2007,2009,
2012,2013,2015,2016,2018
Total 14 13
FIGURE 10 Differences in the zonally averaged zonal wind (blue contours; m s
−1
), cross sections of EP flux (vectors; 10
8
m
2
s
−2
) and its
divergence (red contours; m s
−2
) between positive and negative values of the SICK index during (a) autumn and (b) winter. Dark (light)
shading indicates zonal wind anomalies significant at the 95% confidence level, estimated using the Student's t-test. [Colour figure can be
viewed at wileyonlinelibrary.com]
10 HAN ET AL.
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sea ice. Correspondingly, increased sea ice anomalies
during autumn at Kara Sea are followed by an intensified
equator-to-pole temperature gradient at high latitudes of
Asia and a reduced temperature gradient at midlatitudes
(Figure 11a). Therefore, accelerated westerly and easterly
anomalies occupy the region north to 50N and the
region between 30N and 50N in Asia, respectively,
which facilitates warming temperature anomalies at NEC
(Figure 11b).
4|CONCLUSION AND
DISCUSSIONS
This study investigated the out-of-phase relationship
between autumn sea ice in the Kara Sea and winter mini-
mum temperatures in NEC during the past four decades.
Specifically, diminished autumn sea ice anomalies at the
Kara Sea are followed by dominantly cooling winters at
the NEC. Further results showed that the reduced SICK
is associated with increased release of turbulent heat flux
from the ocean to the atmosphere, resulting in warm air
temperature and ascending motion anomalies in situ.
Consistently, the reduced autumn SICK leads to weak-
ened polar vortex in the troposphere and stratosphere
during winter through vertical propagation of planetary
waves, contributing to a negative phase of the AO. The
SICK is related to an anomalous Rossby wave source at
the Mediterranean via the advection of vorticity by
divergence wind anomalies induced by the
AO. Correspondingly, a Rossby wave train originates
over the Mediterranean Sea and propagates eastward to
Northeast Asia, by which the autumn SICK substan-
tially influences the Mongolia cyclone and the
minimum temperature anomalies in NEC by modula-
tion of AO. In addition, the reduced SICK could lead to
intensified upper-level westerly anomalies in East Asia
through modulation of the equator-to-pole temperature
gradient in Asia and cause cooling anomalies at NEC.
Notably, when the linear influence of the AO is
removed, the weakened polar vortex in the troposphere
and stratosphere has insignificant correlations with the
occurrence of cold winters in NEC (Figure not shown).
In addition, the East Asian winter monsoon has been
regarded as one contributor to air temperature anomalies
over NEC. However, the East Asian winter monsoon has
little influences on the relationship between AO and
Tmin_NEC (Figure S2). These results support the asser-
tion that the AO acts as a bridge linking the autumn
SICK index and minimum temperatures in NEC in the
following winter (Figure S2).
AUTHOR CONTRIBUTIONS
Tingting Han: Conceptualization; methodology;
writing –review and editing. Xin Zhou: Writing –review
and editing; investigation. Shangfeng Li: Conceptualiza-
tion; methodology. Botao Zhou: Conceptualization;
methodology.
ACKNOWLEDGEMENTS
We sincerely acknowledge the anonymous reviewers
whose kind and suggestive comments greatly improve
the quality of this manuscript. We also thank Mr. Qiushi
Zhang from Heilongjiang Sub-Bureau of Northeast Air
Traffic Management Bureau for his valuable comments
in the revision. This work was jointly supported by
Guangdong Major Project of Basic and Applied Basic
Research (Grant No. 2020B0301030004), the National
FIGURE 11 (a) Linear regression pattern of winter 300-hPa temperature gradient (shadings; 10
−7
Cm
−1
) and zonal wind (contours;
ms
−1
) against the autumn SICK index during 1979–2019. Stippled (crossed) areas indicate the equator-to-pole temperature gradient anomalies
that significantly exceed the 95% (90%) confidence level, estimated using the Student's t-test. (b) Linear regression pattern of winter 300-hPa
zonal wind (contours; m s
−1
) against the Tmin_NEC index during 1979–2019. Dark (light) shading indicates zonal wind anomalies significant
at the 95% (90%) confidence level, estimated using the Student's t-test. [Colour figure can be viewed at wileyonlinelibrary.com]
HAN ET AL.11
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Natural Science Foundation of China (Grant No. 41875119)
and Science and Technology Development Plan in Jilin
Province of China (Grant No. 20230203135SF).
CONFLICT OF INTEREST STATEMENT
The authors have not disclosed any competing interests.
DATA AVAILABILITY STATEMENT
The minimum temperature records can be obtained from
CN05.1 dataset. The NCEP/NCAR reanalysis datasets
can be downloaded from https://psl.noaa.gov/data/
gridded/data.ncep.reanalysis.html.
ORCID
Tingting Han https://orcid.org/0000-0002-4749-3792
Xin Zhou https://orcid.org/0009-0009-3581-573X
Botao Zhou https://orcid.org/0000-0002-5995-2378
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How to cite this article: Han, T., Zhou, X., Li, S.,
& Zhou, B. (2024). Influence of autumn Kara Sea
ice on the subsequent winter minimum
temperature over the Northeast China.
International Journal of Climatology,1–13. https://
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