Patterns of Asian Winter Climate Variability and Links to Arctic Sea Ice

BINGYI WU AND JINGZHI SU

Chinese Academy of Meteorological Sciences, Beijing, China

ROSANNE D’ARRIGO

Tree-Ring Laboratory, Lamont-Doherty Earth Observatory, Palisades, New York

(Manuscript received 11 April 2014, in final form 17 June 2015)

ABSTRACT

This paper describes two dominant patterns of Asian winter climate variability: the Siberian high (SH)

pattern and the Asia–Arctic (AA) pattern. The former depicts atmospheric variability closely associated with

the intensity of the Siberian high, and the latter characterizes the teleconnection pattern of atmospheric

variability between Asia and the Arctic, which is distinct from the Arctic Oscillation (AO). The AA pattern

plays more important roles in regulating winter precipitation and the 850-hPa meridional wind component

over East Asia than the SH pattern, which controls surface air temperature variability over East Asia.

In the Arctic Ocean and its marginal seas, sea ice loss in both autumn and winter could bring the positive

phase of the SH pattern or cause the negative phase of the AA pattern. The latter corresponds to a weakened

East Asian winter monsoon (EAWM) and enhanced winter precipitation in the midlatitudes of the Asian

continent and East Asia. For the SH pattern, sea ice loss in the prior autumn emerges in the Siberian marginal

seas, andwinter lossmainly occurs in theBarents Sea, Labrador Sea, andDavis Strait. For theAApattern, sea

ice loss in the prior autumn is observed in the Barents–Kara Seas, the western Laptev Sea, and the Beaufort

Sea, and winter loss only occurs in some areas of the Barents Sea, the Labrador Sea, and Davis Strait. Sim-

ulation experiments with observed sea ice forcing also support that Arctic sea ice loss may favor frequent

occurrence of the negative phase of the AA pattern. The results also imply that the relationship between

Arctic sea ice loss and winter atmospheric variability over East Asia is unstable, which is a challenge for

predicting the EAWM based on Arctic sea ice loss.

1. Introduction

During boreal winter, the strongest continental anti-

cyclone on Earth, known as the Siberian high (SH),

covers the Asian continent. Intense cooling of the air’s

surface layer and sinking motion induced by the mid-

and upper-level convergence contribute to an en-

hancement of the SH (Ding and Krishnamurti 1987;

Ding 1990). The SH strongly affects weather and

climate over Asia and parts of Europe. Outbreaks

of cold polar air westward from the SH pressure

cell cause occasional severe cold spells over areas of

Europe. An example is the winter of 2011/12, when

more than 700 people died due to extreme cold con-

ditions. The SH is an important part of the East Asian

winter monsoon (EAWM) system. The EAWM is a

highly significant feature of Asia’s winter circulation,

closely associated with the development and south-

ward propagation of cold surges over East Asia

(Chang and Lau 1980; Ding 1990; Jhun and Lee 2004;

Wu et al. 2006).

Recent studies have shown a strengthening trend in

the SH over the past two decades (Jeong et al. 2011; Wu

et al. 2011). The corresponding winter surface air tem-

perature (SAT) exhibits a negative trend over the Asian

continent (Cohen et al. 2009, 2012; Wu et al. 2011).

Some regions of Eurasia have recently experienced ex-

ceptionally cold winters, such as in 2007/08, 2009/10,

2010/11, 2011/12, and 2012/13 (Fig. 1). It appears that

cold winters have becomemore frequent over East Asia.

It has been found that lower Arctic sea ice values from

Corresponding author address: Bingyi Wu, Chinese Academy of

Meteorological Sciences, No. 46, Zhong-Guan-Cun SouthAvenue,

Haidian District, Beijing 100081, China.

E-mail: wby@cams.cma.gov.cn

Denotes Open Access content.

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the previous autumn to winter may contribute to cold

conditions over Eurasia and an enhanced SH, via large-

scale dynamic and thermal processes (Dethloff et al.

2006; Francis et al. 2009; Honda et al. 2009; Petoukhov

and Semenov 2010; Screen and Simmonds 2010;

Overland and Wang 2010; Wu et al. 1999, 2011; Francis

and Vavrus 2012; Liu et al. 2012; Jaiser et al. 2012;

Hopsch et al. 2012; Tang et al. 2013; Vihma 2014; Peings

and Magnusdottir 2014; Walsh 2014). Wu et al. (1999)

showed that variability in winter sea ice in the Barents–

Kara Seas is related to the intensity of the EAWM via

the Eurasian teleconnection pattern. This study in-

dicated that heavy sea ice in these seas excites the pos-

itive phase of the Eurasian pattern, with anomalously

low 500-hPa heights over Siberia and positive height

anomalies over East Asia. This pattern weakens the

East Asian trough and the intensity of the EAWM, and

decreases the frequency of cold air outbursts into China.

Opposite effects are observed during light sea ice con-

ditions in this area of the Arctic marginal seas

(Petoukhov and Semenov 2010; Inoue et al. 2012).

Based on simulation experiments with prescribed sea ice

forcing in the Barents–Kara Seas, Petoukhov and

Semenov (2010) suggested that sea ice loss may result in

strong anticyclonic anomalies over the Arctic Ocean,

leading to a continental-scale winter cooling, with more

than a threefold increased probability of cold winter

extremes over Eurasia. Inoue et al. (2012) showed that

FIG. 1. SAT anomalies (8C) for six recent winters, with the linear trend for 1979–2013 period removed (NCEP–NCARReanalysis 1 data).

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light sea ice conditions during winter in the Barents Sea

could lead to an anticyclonic anomaly over the Siberian

coast and cold advection over eastern Siberia.

Francis et al. (2009) and Honda et al. (2009) in-

dependently argued that summer or summer to autumn

sea ice can impact the atmosphere in the ensuing win-

tertime. Wu et al. (2011) showed that persistent autumn

to winter sea ice concentration (SIC) anomalies in the

Barents–Kara Seas and the northern vicinity of these

seas, with concurrent sea surface temperature (SST)

anomalies, are responsible for the SH and SAT anom-

alies over the middle and high latitudes of Eurasia.

However, recent observations and simulation experi-

ments did not support significant impacts of Arctic sea

ice loss on the midlatitudes (Screen et al. 2014; Peings

and Magnusdottir 2014; Walsh 2014). On 16 September

2012, Arctic sea ice reached its minimum extent for the

year, of 3.41 million square kilometers. This is the lowest

seasonal minimum extent in the satellite record since

1979. However, in the ensuing winter (2012/13), the

strength of the SH was nearly normal. Additionally, in

the winter of 2006/07, a weakened SH (its standard de-

viation was below21.0) corresponded to a negative SIC

anomaly in the previous September (see Fig. 2 of Wu

et al. 2011). These two cases imply that autumn sea ice

loss does not always correspond to a strengthened SH

(or enhanced EAWM). Indeed, in addition to Arctic sea

ice, there are many factors that influence the SH, such as

Eurasian snow cover (Cohen et al. 2012) and internal

atmospheric variability. On the other hand, atmospheric

circulation variability showed different regimes in the

two abovementioned winters (see section 3 below).

Thus, it is impossible to predict dominant patterns of

winter atmospheric variability over the Asian continent

in terms of a single external factor. The motivation of

the present study is to explore dominant patterns of

winter atmospheric variability over Asia and their pos-

sible linkages with Arctic sea ice loss. Our study dem-

onstrates that Arctic sea ice loss also promotes the

weakening of the EAWM.

2. Data and methods

The following datasets were used: 1) the Arctic SIC

dataset (18 3 18) from January 1979 to May 2013, ob-

tained from the British Atmospheric Data Centre

(BADC; http://badc.nerc.ac.uk/data/hadisst/); 2) the

monthly mean sea level pressure (SLP), SAT, winds,

and geopotential heights from January 1979 to March

2013, obtained from NCEP–NCAR Reanalysis 1; 3)

monthly mean global land precipitation data from 1979

to 2013 (http://ftp.cpc.ncep.noaa.gov/precip/50yr/gauge/

2.5deg/format_bin/; Chen et al. 2002); and 4) the

monthly mean Arctic Oscillation (AO) index for the

period from 1979 to May 2013 (http://www.cpc.ncep.

noaa.gov/products/precip/CWlink/daily_ao_index/monthly.

ao.index.b50.current.ascii).

Empirical orthogonal function (EOF) analysis was

performed on winter [December–February (DJF)]

mean SLP. The Monte Carlo method was applied to

examine statistical field significance, as in Livezey and

Chen (1983). For an anomalous field derived from linear

regression, the percentage of grid points that are statis-

tically significant at 0.05 (0.01) level is first identified

over a domain. This process is then repeated 1000 times

with different series of 34 (or 34 winters from 1979 to

2013) numbers randomly selected from a normal dis-

tribution. The anomalous field is deemed significant

if the percentage of significant grid points exceeds

that derived from 1000 experimental replications.

Additionally, a Student’s t test was used to assess the

statistical significance of atmospheric changes between

different phases.

Additionally, the ECHAM5 (Roeckner et al. 2003)

model (T63 spectral resolution and 19 pressure levels)

was applied to explore the impacts of SIC on the model

atmosphere. A simulation was performed with observed

monthly SIC in the Northern Hemisphere from January

1978 to November 2012 (419 months) as the external

forcing, while the SIC in the Southern Hemisphere and

global SST were prescribed as their climatological

monthly mean. The SST and SIC data were obtained

through a spatially interpolation of observations taken

from the BADC (http://badc.nerc.ac.uk/data/hadisst/);

for detailed information, refer to the Atmospheric

Model Intercomparison Project (AMIP) phase II SST

andSICboundary condition dataset (http://www-pcmdi.llnl.

gov/projects/amip/AMIP2EXPDSN/BCS/bcsintro.php).

This experiment was repeated with 40 different atmo-

spheric initial conditions that were derived from a 50-yr

control run. In regions where the SIC changes year to

year, the SST was prescribed as its climatological value,

unlike in Screen et al. (2014). In this study, all linear

trends in the original data were first removed before

performing EOF, regression, and composite differential

analyses.

3. Two dominant patterns and their impacts

This study focuses on winter (DJF) SLP variability in

the middle and high latitudes in order to reveal domi-

nant patterns of winter atmospheric variability over the

Asian continent. EOF analysis was applied to the nor-

malized area-weighted winter mean SLP data (after

detrending) over 308–708N, 808–1208E and for 34 winters

from 1979 to 2013. This domain contains the core region

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of the SH where the regionally averaged winter SLP

over 408–608N, 808–1208E is used as the SH index (SHI)

to characterize the intensity of the SH (Wu and

Wang 2002).

The first two EOFs (EOF1 and EOF2) respectively

account for 50% and 26% of the variance. For the

leading EOF, Figs. 2a–d show anomalies in winter mean

SLP, 500-hPa height, SAT, and 850-hPa meridional

wind components, derived from linear regressions on

the normalized leading principal component (PC1). In

the middle and high latitudes of Eurasia, winter SLP

anomalies show a monopole structure, with the positive

center located over the Ural Mountains, indicating a

strengthened SH (Fig. 2a). Meanwhile, negative SLP

anomalies are seen in themiddle and low latitudes of the

Asian continent. Winter mean 500-hPa height anoma-

lies show a triple structure: the center of positive height

anomalies is over the Kara Sea, and two negative centers

are located over Europe and northeastern Asia. Thus,

Fig. 2b indicates a westward shifted and strengthened

500-hPa East Asian trough. Height anomalies here

closely resemble those in Jung et al. (2014; see their

Fig. 3), who investigated the impact of the Arctic on

winter 500-hPa height in the midlatitudes. Negative

SAT anomalies over most of East Asia are dynamically

consistent with the strengthened SH and deepened East

FIG. 2. (a) Regression map of detrended winter mean SLP, regressed on the normalized PC1 of EOF analysis of

detrended winter mean SLP variability over 308–708N, 808–1208E (outlined in green); the yellow (light blue) and red

(blue) areas indicate positive (negative) SLP anomalies at 0.05 and 0.01 significance levels, respectively. (b)–(d)As in

(a), but for detrended winter 500-hPa height, SAT, and 850-hPa meridional wind components, respectively; contour

intervals are 0.5 hPa in (a), 10 gpm in (b), 18C in (c), and 0.3m s21 in (d).

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Asian trough (Fig. 2c). Meanwhile, positive SAT

anomalies cover the Arctic. Consequently, Figs. 2a–c

characterize atmospheric circulation anomalies associ-

ated with the SH, supported by the correlation between

the PC1 and the detrended SHI (r 5 0.95; see Fig. 3).

This systematic atmospheric circulation anomaly is

herein termed the SH pattern. The 850-hPa meridional

wind anomalies, however, do not exceed the level of

statistical significance over eastern China south of 308N(Fig. 2d). Strengthened northerlies are seen over the

area from Lake Baikal extending southeastward to the

northwestern Pacific. When accompanied by a strength-

ened SH, weak southerly anomalies emerge over parts

of southern China. The SH pattern mainly reflects large-

scale meridional circulation anomalies over the middle

and high latitudes.

The same analysis process was carried out over three

different domains: 1) 308–708N, 508–1308E, 2) 308–808N,

508–1308E, and 3) 208–708N, 608–1308E. The leading

EOFs over the three domains respectively account for

46%, 46%, and 44% of the variance. Corresponding

winter SLP, 500-hPa height, SAT, and 850-hPa meridi-

onal wind anomalies, derived from linear regressions on

their PC1s, closely resemble those shown in Figs. 2a–d (not

shown). Their PC1s are significantly correlated with the

detrended SHI, with correlations of 0.90, 0.75, and 0.81,

respectively (Fig. 3).

For EOF2, the amplitudes of both positive SLP and

500-hPa height anomalies are weaker than those for

EOF1 (Figs. 4a,b). Positive SLP anomalies appear over

most of the Asian continent south of 508N, with

moderate negative anomalies in the north. Negative

SLP and 500-hPa height anomalies mainly appear in

the Arctic and northern North Pacific, making this

pattern distinct from the positive phase of the AO. In

fact, the correlation between EOF2 and the detrended

AO is 0.28 (0.44) for winters of 1979/80–2008/09 (win-

ters of 1979/80–2012/13). Positive SAT anomalies are

observed in the middle and high latitudes of the Asian

continent, with negative SAT anomalies to the south

(Fig. 4c). Such a spatial distribution of SAT anomalies

is dynamically consistent with that for the SLP anom-

alies. Significant anomalies in 850-hPa meridional

winds are observed in East Asia, particularly in eastern

and northeastern China (Fig. 4d).

The second EOFs over the three domains (308–708N,

508–1308E; 308–808N, 508–1308E; and 208–708N, 608–1308E) respectively account for 22%, 23%, and 22% of

the variance. Anomalies in detrended winter SLP,

500-hPa height, SAT, and 850-hPa meridional winds,

derived from linear regressions on their PC2s, closely

resemble those shown in Figs. 4a–d for the domain 308–808N, 508–1308E, but with anomalies of opposing sign for

the other domains (not shown). For two domains (308–708N, 508–1308Eand 208–708N, 608–1308E), the PC2 time

series are out of phase with that for 308–708N, 808–1208E(Fig. 5); their correlations are 20.95 and 20.90, re-

spectively. Over the domains 308–708N, 808–1208E and

308–808N, 508–1308E, the PC2 time series are in phase

(Fig. 5; their correlation is 0.84). Although the domains

are different for EOF analysis, the atmospheric circu-

lation anomalies associated with EOF2s exhibit similar

FIG. 3. Normalized time series of the detrended winter SHI (red line) and the PC1s of EOF

analyses of detrended winter mean SLP variability over four different domains: 308–708N, 808–1208E (green); 308–708N, 508–1308E (blue); 308–808N, 508–1308E (black); and 208–708N, 608–1308E (purple).

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features over Asia and the Arctic. Thus, this systematic

circulation anomaly is herein termed the Asia–Arctic

(AA) pattern. The AA pattern predominantly exhibits

large-scale zonal circulation anomalies, differing from

the SHpattern. Because the intensity of the SH is closely

related to the EAWM variability (Jhun and Lee 2004;

Wu et al. 2006), the extracted leading SLP pattern

should characterize SH variability as accurately as pos-

sible. If we selected a domain that covers more area of

the Arctic Ocean, the correlation between the leading

SLP pattern and the detrended SHI would decline.

Consequently, the first two PCs of EOF analysis over

308–708N, 808–1208E can be regarded as indices that

depict the SH and AA patterns, respectively. It should

be pointed out that the SH and AA pattern well

represent the first two coupled patterns between winter

mean SLP over Eurasia (308–708N, 508–1308E) and any

meridional vertical cross section of zonal winds over the

Asian continent (extracted by the maximum covariance

analysis; not shown). Thus, neither of the SH and AA

patterns relies on the EOF method. They reflect differ-

ent dynamic regimes, namely large-scale meridional and

zonal circulation anomalies.

The regionally averaged winter 850-hPa meridional

wind over eastern China (308–408N, 1108–1208E) is sig-nificantly correlated with the AA pattern (r 5 20.56;

after removing linear trends, the correlation is 20.60,

at 0.01 significance level). In contrast to the AA pattern,

the SH pattern does not show a significant relationship

with the regionally averaged 850-hPa meridional wind

FIG. 4. As in Fig. 2, but regressed on the normalized PC2 of EOF analysis of detrendedwintermean SLP variability

over 308–708N, 808–1208E [outlined by green lines in (a)]. Contour intervals are 0.5 hPa in (a), 5 gpm in (b), 0.58C in

(c), and 0.3m s21 in (d).

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(r 5 20.20 after detrending). At the surface, however,

the regionally averaged meridional wind (at 10m) over

29.528–40.958N, 110.6258–1208E is significantly corre-

lated with the SH and AA patterns after detrending

at 20.51 and 20.57, respectively. This implies that

compared with the SH pattern, the AA pattern shows a

closer relationship with the EAWM.

To investigate dynamical connections to Arctic atmo-

sphere variability, we first selected positive and negative

phasewinters for which their standard deviations are.0.8

or,20.8, as shown in Table 1. We selected the latitude–

pressure vertical cross section at 1108E to show wave ac-

tivity fluxes (Fig. 6). In the middle and high troposphere

south of 758N, the wave activity fluxes show coherent

propagations southward to 458N, reflecting a dynamical

linkage between the Arctic and the midlatitudes of East

Asia (Fig. 6a). In the Arctic north of 758N, the wave ac-

tivity fluxes propagate northward over nearly the entire

troposphere. For the AA pattern, southward propagation

is mainly observed between 408 and 658N (Fig. 6b), in-

dicating that sub-Arctic atmospheric variability is directly

linked with that over the midlatitudes of East Asia via

atmospheric energy propagations. Over the Arctic, the

wave activity fluxes display northward propagations. At

500hPa, the wave activity fluxes originated from the

Barents–Kara Seas propagate southeastward to the high

latitudes of the Asian continent, and then propagate

northeastward to the Arctic Ocean (Fig. 6c). Another

branch propagates to the northwestern Pacific.

Figure 7 shows the latitude–pressure vertical cross

section of westerly anomalies associated with the SH

and AA patterns along 1108E. Relative to the negative

phase of the SH pattern, its positive phase corresponds

to a strengthened westerly jet in the higher troposphere

(Fig. 7a) (the center of the westerly jet at 1108E is

around 308N and 200hPa; not shown). Meanwhile, over

the middle and high latitudes, westerly winds are

weakened significantly. Consequently, weakened west-

erlies over the middle and high latitudes favor cold air

accumulation and outbreaks southward from the Arctic

and high latitudes, which enhance strength of both of the

SH and the East Asian trough (Figs. 2a,b), leading to

negative SAT anomalies over East Asia (Fig. 2c), and

vice versa for its negative phase. For the AA pattern,

amplitudes of westerly anomalies are apparently weaker

relative to the SH pattern (Fig. 7a) and coherently shift

northward (Fig. 7b). Relative to the positive phase of the

AA pattern, its negative phase corresponds to

strengthened westerlies between 358 and 558N, with

weakened westerlies on both sides. The spatial distri-

bution of westerly anomalies from 408 to 808N is dy-

namically consistent with negative height anomalies in

the middle and high latitudes in Fig. 6b. Weakened

tropospheric westerlies over the high latitudes of the

Asian continent favor Arctic cold air into the Asian

continent and accumulation, resulting in positive SLP

anomalies over the northern Asian continent (as in

Figs. 4a,c, but with opposing sign). Meanwhile,

strengthened tropospheric westerlies between 358 and558N obstruct cold air accumulation and outbreaks

southward from the midlatitudes, leading to positive

SAT and negative SLP anomalies over the middle and

FIG. 5. Normalized PC2s of EOF analyses of detrended winter mean SLP variability over

four different domains: 308–708N, 808–1208E (green); 308–708N, 508–1308E (blue; multiplied by

21.0); 308–808N, 508–1308E (black); and 208–708N, 608–1308E (purple, multiplied by 21.0).

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low latitudes of the Asian continent. Opposite anoma-

lous fields are observed for the positive phase of the AA

pattern.

Impacts of the two atmospheric patterns on winter

precipitation differ, as shown in Fig. 8. The positive

phase of the SH pattern causes decreases in winter

precipitation over most of the Asian continent, partic-

ularly in the low latitudes east of 808E, whereas in-

creased precipitation is mainly observed in some areas

of the midlatitudes and the Russian Far East (Fig. 8a).

The AA pattern has more substantial impacts on winter

precipitation than the SH pattern (Fig. 8b). Compared

with the positive phase of the AA pattern, its negative

phase significantly enhances precipitation over East

Asia and central Asia. Enhanced precipitation is dy-

namically consistent with 500-hPa height anomalies in

Fig. 6c. Negative 500-hPa height anomalies over the

middle and high latitudes of Asia and positive height

anomalies over the northwestern and northern Pacific

favor increased precipitation between them. Mean-

while, decreased precipitation emerges in the high lati-

tudes and between 708 and 1108E south of 408N.

4. Possible associations with sea ice loss in autumnand winter

Both the SH and AA patterns are associated with

Arctic SIC anomalies in the prior autumn [September–

November (SON)] to winter (DJF) (Fig. 9). For the SH

pattern, decreased autumn SIC is observed in the Sibe-

rian marginal seas (Fig. 9a), particularly from the

northern Barents Sea across to the Kara Sea, extending

eastward to the Laptev Sea and the Pacific part of the

Arctic Ocean. In winter, negative SIC anomalies are

mainly observed in the Greenland–Barents–Kara Seas,

the Labrador Sea, and Davis Strait (Fig. 9b), dynami-

cally consistent with the spatial distribution of winter

surface wind anomalies (not shown). Wu et al. (2011)

suggested that the regionally averaged (76.58–83.58N,

60.58–149.58E) September SIC is significantly correlated

with the ensuing winter SIC averaged over the Barents–

Kara Seas (67.58–80.58N, 20.58–80.58E) during the pe-

riod from 1979 to 2010 (r5 0.66; correlation is 0.52 after

detrending). Thus, the regionally averaged September

SIC is a potential precursor for the ensuing winter SH

that cannot be predicted using tropical SSTs alone [their

correlation was 20.6 after detrending; see Fig. 2 of Wu

et al. (2011)]. An enhanced SH is associated with per-

sistent SIC negative anomalies from autumn to winter,

and previous observations and simulation experiments

support this association (Honda et al. 2009; Petoukhov

and Semenov 2010; Wu et al. 1999; Liu et al. 2012; Inoue

et al. 2012; Rinke et al. 2013; Jung et al. 2014).

For the negative phase of the AA pattern, negative

SIC anomalies in the previous autumn are observed in

some areas: the Barents–Kara Seas, the western Laptev

Sea, the East Siberian Sea, and the Beaufort Sea

(Fig. 9c). In winter, increased SIC in the Greenland Sea

and southeastern Barents Sea is concurrent with de-

creased SIC in the Labrador Sea, Davis Strait, and some

areas of the Barents Sea (Fig. 9d), unlike in Fig. 9b.

Amplitudes and extents of SIC anomalies associated

with the AA pattern are smaller relative to the SH

pattern. Linear regression analyses further verify the

associations between the two atmospheric patterns and

previous autumn SIC anomalies (not shown).

In the data and methods section, simulation experi-

ments forced by SIC forcing were introduced. Here we

examine simulated winter atmospheric responses

(Figs. 10 and 11) and corresponding autumn and winter

SIC anomalies (Fig. 12). Data used here are original

model output and SIC data rather than detrended data.

Figure 10 shows differences in simulated winter mean

atmospheric circulation between the positive and neg-

ative phases of the SH pattern during the period from

1978 to 2012. It is seen that positive SLP anomalies are

mainly observed over the Arctic, much of North

America, and the Tibetan Plateau, and significant neg-

ative SLP anomalies emerge over East Asia and the

Russian Far East (Fig. 10a). At 500 hPa, positive geo-

potential height anomalies appear over the Arctic and

are surrounded by negative anomalies (Fig. 10b). A

shifted northward and deepened East Asian trough

emerge over the East Asian coast, with positive height

anomalies over the midlatitudes of the Asian continent

and the northwestern Pacific sector. Significant positive

SAT anomalies are mainly confined to the high lati-

tudes, and positive SAT anomalies occupy most of

TABLE 1.Winter cases with standard deviations.0.8 (positive phase) or,20.8 (negative phase). Boldface indicates winters with both the

SH and AA patterns.

Pattern Positive phase Negative phase

SH 1980/81, 1983/84, 1984/85, 1985/86, 2004/05, 2005/06,

and 2011/12

1988/89, 1991/92, 1992/93, 1996/97, 1997/98, 2003/04,

and 2006/07

AA 1982/83, 1983/84, 1994/95, 1995/96, 1999/2000, 2001/02,

2007/08, and 2011/12

1984/85, 1989/90, 2000/01, 2004/05, 2008/09, 2009/10,

and 2012/13

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FIG. 6. (a) Differences in detrended mean geopotential heights between the positive and

negative phases of the SH pattern along the latitude–pressure cross section at 1108E, super-imposed on meridional and vertical (multiplied by 0.05) wave activity flux (vectors; m2 s22) of

Takaya and Nakamura (2001); light blue and blue areas indicate geopotential height differ-

ences at 0.05 and 0.01 significance levels, respectively. (b) As in (a), but for differences in

detrended mean geopotential heights between the negative and positive phases of the AA

pattern. (c) Differences in detrended mean geopotential heights between the negative and

positive phases of the AA pattern at 500 hPa; contour intervals are 20 gpm; composite winter

cases for the SH and AA patterns are shown in Table 1 (nonboldface winters).

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Eurasia except for some areas of northern Eurasia and

the Tibetan Plateau where negative SAT anomalies are

visible (Fig. 10c). Thus, simulated winter atmospheric

circulation and SAT anomalies indicate a weakened

EAWM; to a great extent, they capture major charac-

teristics of the negative phase of the AA pattern as

shown in Fig. 4, but with opposing sign.

Figure 11 shows simulated differences between the

negative and positive phases of the AA pattern. Signif-

icant positive SLP anomalies emerge over the Arctic

and Siberia (Fig. 11a), and positive 500-hPa height

anomalies appear over northern Eurasia and the Arctic,

with concurrent negative height anomalies over Europe,

East Asia, and the northern North Pacific, implying a

strengthened East Asian trough (Fig. 11b). At the sur-

face, positive SAT anomalies are observed over the

Arctic and negative SAT anomalies occupy much of

Eurasia, particularly over the central Asian continent

and East Asia, where significant negative SAT anoma-

lies are observed (Fig. 11c). Thus simulated atmospheric

circulation anomalies, to a great extent, reflect the

positive phase of the SH pattern rather than the AA

pattern.

A stronger atmospheric response is seen in Fig. 11 rel-

ative to that in Fig. 10, indicated by amplitudes and ex-

tents of both of positive SLP and 500-hPa height

anomalies. Differences in mean SIC forcing may be re-

sponsible for different atmospheric responses. It is seen

that although SIC data used in Figs. 9a,b and 12a,b are

derived from the BADC (http://badc.nerc.ac.uk/data/

hadisst/) their differences are visible, particularly in the

prior autumn, duemainly to detrended data used in Fig. 9.

In Fig. 12a, negative SIC anomalies are mainly observed

in from the Barents Sea eastward to the Laptev Sea, and

positive SIC anomalies emerge from theEast Siberian Sea

eastward to the Beaufort Sea. Nearly opposite SIC

anomalies are seen in part of the Laptev Sea eastward to

the Beaufort Sea and the Arctic Ocean (Fig. 12c). The

area with SIC anomalies #25.0% is approximately

2.203 106km2 in Fig. 12a and less than 3.043 106km2 in

Fig. 12c. Thus, compared to Fig. 12a, SIC anomalies in

Fig. 12c correspond to more heating released to the

FIG. 7. (a) Differences in detrended mean westerly flows (m s21) between the positive and

negative phases of the SH pattern along the latitude–pressure cross section at 1108E. (b) As in

(a), but for differences in detrended mean westerly flows between the negative and positive

phases of the AA pattern; the composite cases for the SH and AA patterns and meanings for

the color areas are as in Fig. 6

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atmosphere from the ocean, which enhances the feedback

of sea ice loss on the winter atmosphere. Thus, simulated

strengthening of SH (Fig. 11) is reasonable. Above ana-

lyses demonstrate that Arctic sea ice loss could either

bring the positive phase of the SH pattern or produce the

negative phase of the AA pattern, which corresponds to a

weakened EAWM. Thus, it is a challenge to predict

EAWM based on Arctic sea ice loss. Additionally, simu-

lation results also show that Arctic sea ice loss also favors

occurrences of cold winters in North America.

On the other hand, very weak differences in simu-

lated winter SLP and 500-hPa height indicate low co-

herency of model results, and simulated time series of

the SHI averaged over the 40 experiments are strictly

confined to a very narrow range of 1029–1031 hPa

(Fig. 13a; the observed SHI range was 1026.8–

1033.5 hPa from 1979 to 2013). The simulated standard

deviation of the SHI ranges from 1.1 to 2.1 hPa

(Fig. 13b). Such low coherency reflects the com-

bined effects of large internal variability and model

FIG. 8. (a) Anomalous winter precipitation percentages, derived from differences in de-

trended winter mean precipitation between the positive and negative phases of the SH pattern

divided by the winter mean precipitation averaged over the past 34 winters from 1979 to 2013;

thin (dashed thin) and thick (dashed thick) purple contours indicate positive (negative) pre-

cipitation differences exceeding 0.05 and 0.01 significance levels, respectively. (b) As in (a), but

differences in detrendedwintermean precipitation between the negative and positive phases of

the AA pattern; composite cases are listed in Table 1 (nonboldface winters).

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uncertainties. Screen et al. (2014) also suggested that

SLP and height responses are hard to detect and may be

partially or totally masked by atmospheric internal

variability. Additionally, the atmospheric response to

sea ice loss may depend on the state of the atmosphere

(Balmaseda et al. 2010).

It is seen that simulated SLP and 500-hPa height

anomalies exhibit a quasi-barotropic structure over the

Arctic (Figs. 10 and 11). This differs from the response

of winter atmosphere to SIC loss in Screen et al. (2014)

and Peings and Magnusdottir (2014) where a baroclinic

response was evident over the Arctic Ocean. Screen

et al. (2014) discussed possible reasons for the existence

of negative winter SLP anomalies over the Arctic in

response to sea ice loss, which differs from previous

results (Alexander et al. 2004; Francis et al. 2009; Liu

et al. 2012). They suggested that ensemble member

numbers and prescription of SIC forcing in simulation

experiments may be the reason for negative SLP

anomalies in response to SIC loss.

Prior researchers discussed possible mechanisms for

the impact of Arctic sea ice on the atmosphere: a de-

creased autumn Arctic sea ice would lead to an in-

tensified heat loss from the ocean and a stronger

FIG. 9. Differences in detrended mean SIC between the positive and negative phases of the SH pattern in (a) the

previous autumn (SON) and (b) winter (DJF). (c),(d) As in (a),(b), but for differences between the negative and

positive phases of the AA pattern; green contours denote SIC differences at 0.05 significance level. The composite

cases for the SH and AA patterns are shown in Table 1 (nonboldface winters).

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heating effect to the overlying atmosphere, which

would strengthen atmospheric baroclinicity and in-

stability (Alexander et al. 2004; Jaiser et al. 2012;

Porter et al. 2012). As the seasons progress, through a

negative feedback process, baroclinic atmospheric

processes diminish and barotropic interactions in-

tensify, resulting in winter positive SLP and 500-hPa

height anomalies over the high latitudes and the Arctic

(Alexander et al. 2004; Deser et al. 2004, 2007;

Magnusdottir et al. 2004; Jaiser et al. 2012; Wu et al.

2011; Walsh 2014), favoring a strengthened SH (the

positive phase of the SH pattern). Additionally, Arctic

sea ice loss in both autumn and winter would decrease

the thermal gradient between the Arctic and the

middle and high latitudes of Eurasia, leading to

weakened westerlies in winter (Francis and Vavrus

2012; see Fig. 4 of Wu et al. 2011) and favoring cold air

outbreaks southward from the Arctic. This is the

possible mechanism responsible for the association

between the SH pattern and Arctic sea ice loss. The

similar mechanism may be at work for the linkage

between the AA pattern and Arctic sea ice loss.

FIG. 10. (a) Simulated differences in winter mean SLP between positive and negative phases of the SH pattern (see

Table 1, nonboldface winters), derived from 40 experiments, thus positive and negative phases contain 120 and 280

winters, respectively; thin (dashed thin) and thick (dashed thick) purple contours denote positive (negative) SLP

anomalies at 0.05 and 0.01 significance levels, respectively. (b),(c) As in (a), but for winter 500-hPa height and SAT

differences, respectively; contour intervals are 0.2 hPa in (a), 2 gpm in (b), and 0.28C in (c).

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Additionally, the simulated wave activity flux induced

by sea ice loss also supports the dynamical connection

between the Arctic and Asia (not shown). Recent

studies, however, suggested that although sea ice loss

affects atmospheric variability at northern midlati-

tudes, results show big differences in the magnitude,

timing, and spatial extent of these effects (e.g., Vihma

2014). In addition to the state of the atmosphere

(Balmaseda et al. 2010), differences in the magnitude

and extent of SIC anomalies in Fig. 12 may be one of

the reasons for different remote responses, supported

by Petoukhov and Semenov (2010). We have not ex-

plored possible mechanisms for the impact of SIC loss

on the SH and AA patterns herein because they are

beyond the scope of the present study.

In the late 1990s, the Arctic surface wind fields ex-

perienced an interdecadal shift in both spring (April–

June) and summer (July–September). An anomalous

cyclone prevailed before 1997 and was then replaced

by an anomalous anticyclone over the Arctic Ocean,

which was consistent with the rapid decline in trend of

September sea ice extent (Wu et al. 2012). Autumn

Arctic SIC also experienced an interdecadal shift in

the late 1990s, which is one of the possible reasons for

interdecadal variability of winter SAT in East Asia

(Yang and Wu 2013). Although the two AA patterns,

FIG. 11. As in Fig. 10, but for the AA pattern (see Table 1, nonboldface winters and excluding the winter of 2012/13),

derived from 40 experiments, thus negative and positive phases contain 160 and 240 winters, respectively.

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derived respectively from the detrended and original

winter mean SLP data over the same domain, are

highly correlated (0.94; 0.99 after detrending), their

low-frequency evolutions are different (Fig. 14). The

AA pattern derived from the original data showed an

interdecadal shift more clearly in the late 1990s. It is

seen that positive phases of the low-frequency oscil-

lations (.11 yr) were dominant before the winter of

1998/99 and were then replaced by frequent negative

phases. This interdecadal shift was also reflected in a

7-yr running mean time series of the AA pattern. Since

2007 autumn SIC has maintained negative anomalies

in the Arctic Ocean and Siberian marginal seas (not

shown), favoring the occurrence of the negative phase

of the AA pattern.

5. Conclusions and discussion

Using EOF analysis of wintermean SLP variability over

the Asian continent, we have described two dominant

patterns of winter atmospheric variability: the SH andAA

patterns, which account for 76% of the variance over 308–708N, 808–1208E. The SH pattern depicts well the domi-

nant features of winter atmospheric circulation variability

closely associated with the intensity of SH. The positive

phase of the SH pattern corresponds to a systematic

FIG. 12. Differences in mean SIC in the forced model simulation between the positive and negative phases of the

SH pattern in (a) the previous autumn (SON) and (b) winter (DJF). (c),(d) As in (a),(b), but for differences between

the negative and positive phases of the AA pattern. The composite cases for the SH and AA patterns are same as

those in Figs. 10 and 11, respectively.

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strengthening of both the SH and East Asian trough,

leading to negative SAT anomalies over East Asia, and

vice versa for its negative phase. Decreased autumnArctic

SIC along the Siberian marginal seas, particularly in the

northern Barents–Kara Seas and the Pacific area of the

Arctic Ocean, provides favorable external forcing for

generating the positive phase of this pattern.

The AA pattern features a teleconnection pattern of

atmospheric variability between Asia and the Arctic.

The positive phase of the AA pattern describes positive

SLP anomalies south of 558N and negative SLP anom-

alies in the high latitudes and the Arctic, corresponding

to a strengthened EAWM, and SAT anomalies with

opposing sign emerge over the Asian continent. The AA

pattern is more important than the SH pattern in regu-

lating winter precipitation and the 850-hPa meridional

wind component over East Asia. The negative phase of

the AA pattern may be associated with decreased au-

tumn Arctic SIC in the Barents–Kara Seas, the western

Laptev Sea, and the Beaufort Sea. Simulation experi-

ments with observed SIC forcing also support that

Arctic sea ice loss could bring the positive phase of the

SH pattern or produce the negative phase of the AA

pattern, which corresponds to a weakened EAWM.

Recently, the two dominant patterns have alternately

occurred to influence East Asia. It appears that the AA

pattern has become more frequent recently. Simulation

experiments also indicate that Arctic sea ice loss favors

occurrences of cold winters in North America.

This study has described a statistical association be-

tween Arctic SIC loss and the AA pattern, although

physical details of their association need to be further

investigated. Additionally, factors such as SSTs in the

North Atlantic and subarctic seas and Eurasian snow

cover also play roles in regulating East Asian winter

climate variability (Li 2004; Peng et al. 2003; Cohen et al.

2012; Walsh 2014), and their relative contributions also

warrant further study. The results herein imply that

autumn Arctic sea ice loss plays an important role in

regulating winter climate variability over East Asia.

Acknowledgments. The authors are grateful to all

anonymous reviewers for their insight and constructive

suggestions, which helped to significantly improve this

FIG. 13. (a) Time series of the simulated winter SHI (hPa) averaged over 40 experiments (solid

blue line); dashed blue and red lines denote a mean of the simulated winter SHI averaged over the

past 34 winters from 1978 to 2012 and 5-yr runningmeans of the simulated winter SHI, respectively.

(b) The standard deviation time series of the simulated winter SHI derived from 40 experiments.

6856 JOURNAL OF CL IMATE VOLUME 28

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paper. The authors thank the British Atmospheric Data

Centre (BADC),NCEP–NCAR, and theNOAA/Climate

Prediction Center for providing sea ice concentration

data, atmospheric reanalysis data, and the global land

precipitation data and AO index. This study was sup-

ported by the National Key Basic Research Project of

China (2013CBA01804 and 2015CB453200), the Na-

tional Natural Science Foundation of China (41475080,

41221064), and State Oceanic Administration Project

(201205007).

REFERENCES

Alexander, A., S. Bhatt, and J. Walsh, 2004: The atmospheric

response to realistic Arctic sea ice anomalies in an

AGCM during winter. J. Climate, 17, 890–905, doi:10.1175/

1520-0442(2004)017,0890:TARTRA.2.0.CO;2.

Balmaseda, M. A., L. Ferranti, F. Molteni, and T. N. Palmer, 2010:

Impact of 2007 and 2008 Arctic ice anomalies on the atmo-

spheric circulation: Implications for long-range predictions.

Quart. J. Roy. Meteor. Soc., 136, 1655–1664, doi:10.1002/qj.661.

Chang, C.-P., and K.-M. Lau, 1980: Northeasterly cold surges and

near-equatorial disturbances over the winter MONEX area

during December 1974. Part II: Planetary-scale aspects. Mon.

Wea. Rev., 108, 298–312, doi:10.1175/1520-0493(1980)108,0298:

NCSANE.2.0.CO;2.

Chen, M., P. Xie, J. E. Janowiak, and P. A. Arkin, 2002: Global

land precipitation: A 50-yr monthly analysis based on gauge

observations. J. Hydrometeor., 3, 249–266, doi:10.1175/

1525-7541(2002)003,0249:GLPAYM.2.0.CO;2.

Cohen, J., M. Barlow, and K. Saito, 2009: Decadal fluctuations

in planetary wave forcingmodulate global warming in late boreal

winter. J. Climate, 22, 4418–4426, doi:10.1175/2009JCLI2931.1.

——, J. Furtado, M. Barlow, V. Alexeev, and J. Cherry, 2012:

Arctic warming, increasing snow cover and widespread boreal

winter cooling. Environ. Res. Lett., 7, 014007, doi:10.1088/

1748-9326/7/1/014007.

Deser, C., G. Magnusdottir, R. Saravanan, and A. Phillips, 2004:

The effects of NorthAtlantic SST and sea ice anomalies on the

winter circulation in CCM3. Part II: Direct and indirect

components of the response. J. Climate, 17, 877–889,

doi:10.1175/1520-0442(2004)017,0877:TEONAS.2.0.CO;2.

——, A. Tomas, and S. Peng, 2007: The transient atmospheric

circulation response to North Atlantic SST and sea ice

anomalies. J. Climate, 20, 4751–4767, doi:10.1175/JCLI4278.1.Dethloff, K., and Coauthors, 2006: A dynamical link between the

Arctic and the global climate system. Geophys. Res. Lett., 33,

L03703, doi:10.1029/2005GL025245.

FIG. 14. (a) The low-frequency oscillation (.11 yr) time series of the AA pattern (derived

from detrended data; blue line), derived from a harmonic analysis; the red line is a 7-yr running

mean time series of the AA pattern. (b) As in (a), but the AA pattern was derived from the

original winter mean SLP data. Units are arbitrary.

1 SEPTEMBER 2015 WU ET AL . 6857

Unauthenticated | Downloaded 12/08/21 01:12 AM UTC

Ding, Y., 1990: Build-up, air mass transformation and propagation

of Siberian high and its relation to cold surge in East Asia.

Meteor. Atmos. Phys., 44, 281–292, doi:10.1007/BF01026822.

——, and T. N. Krishnamurti, 1987: Heat budget of the Siberian

high and the winter monsoon.Mon.Wea. Rev., 115, 2428–2449,

doi:10.1175/1520-0493(1987)115,2428:HBOTSH.2.0.CO;2.

Francis, J. A., and S. J. Vavrus, 2012: Evidence linking Arctic

amplification to extreme weather in mid-latitudes. Geophys.

Res. Lett., 39, L06801, doi:10.1029/2012GL051000.

——, W. Chan, D. J. Leathers, J. R. Miller, and D. E. Veron, 2009:

Winter Northern Hemisphere weather patterns remember

summer arctic sea-ice extent.Geophys. Res. Lett., 36, L07503,

doi:10.1029/2009GL037274.

Honda, M., J. Inous, and S. Yamane, 2009: Influence of low Arctic

sea-ice minima on anomalously cold Eurasian winters. Geo-

phys. Res. Lett., 36, L08707, doi:10.1029/2008GL037079.

Hopsch, S., J. Cohen, and K. Dethloff, 2012: Analysis of a link be-

tween fall Arctic sea ice concentration and atmospheric pat-

terns in the following winter. Tellus, 64A, 18624, doi:10.3402/

tellusa.v64i0.18624.

Inoue, J., M. E. Hori, and K. Takaya, 2012: The role of Barents Sea

ice in the wintertime cyclone track and emergence of a warm-

Arctic cold-Siberian anomaly. J. Climate, 25, 2561–2568,

doi:10.1175/JCLI-D-11-00449.1.

Jaiser, R., K. Dethloff, D. Handorf, A. Rinke, and J. Cohen,

2012: Impact of sea ice cover changes on the Northern

Hemisphere atmospheric winter circulation. Tellus, 64A,

11595, doi:10.3402/tellusa.v64i0.11595.

Jeong, J., T. Ou, H. Linderholm, B. Kim, S. Kim, J. Kug, and

D. Chen, 2011: Recent recovery of the Siberian high intensity.

J. Geophys. Res., 116, D23102, doi:10.1029/2011JD015904.

Jhun, J. G., and E. J. Lee, 2004: A new East Asian winter monsoon

index and associated characteristics of the winter monsoon.

J. Climate, 17, 711–726, doi:10.1175/1520-0442(2004)017,0711:

ANEAWM.2.0.CO;2.

Jung, T., M. A. Kasper, T. Semmler, and S. Serrar, 2014: Arctic

influence on subseasonal midlatitude prediction. Geophys.

Res. Lett., 41, 3676–3680, doi:10.1002/2014GL059961.

Li, S., 2004: Impact of northwest Atlantic SST anomalies on the

circulation over the Ural Mountains during early winter.

J. Meteor. Soc. Japan, 82, 971–988, doi:10.2151/jmsj.2004.971.

Liu, J. P., J. A. Curry, H. J. Wang, M. R. Song, and R. M. Horton,

2012: Impact of declining Arctic sea ice on winter snowfall.

Proc. Natl. Acad. Sci. USA, 109, 4074–4079, doi:10.1073/

pnas.1114910109.

Livezey, R., and W. Y. Chen, 1983: Statistical field significance

and its determination by Monte Carlo techniques. Mon.

Wea. Rev., 111, 46–59, doi:10.1175/1520-0493(1983)111,0046:

SFSAID.2.0.CO;2.

Magnusdottir, G., C. Deser, and R. Saravanan, 2004: The effects of

North Atlantic SST and sea ice anomalies on the winter cir-

culation in CCM3. Part I: Main features and storm track

characteristics of the response. J. Climate, 17, 857–876,

doi:10.1175/1520-0442(2004)017,0857:TEONAS.2.0.CO;2.

Overland, J. E., and M. Wang, 2010: Large-scale atmospheric cir-

culation changes are associated with the recent loss of Arctic

sea ice.Tellus, 62A, 1–9, doi:10.1111/j.1600-0870.2009.00421.x.Peings, Y., and G. Magnusdottir, 2014: Response of the wintertime

Northern Hemisphere atmospheric circulation to current and

projectedArctic sea ice decline:Anumerical studywithCAM5.

J. Climate, 27, 244–264, doi:10.1175/JCLI-D-13-00272.1.

Peng, S., W. A. Robinson, and S. Li, 2003: Mechanisms for the NAO

response to the North Atlantic SST tripole. J. Climate, 16, 1987–2004, doi:10.1175/1520-0442(2003)016,1987:MFTNRT.2.0.CO;2.

Petoukhov,V., andV.Semenov, 2010:A linkbetween reducedBarents-

Kara sea ice and cold winter extremes over northern continents.

J. Geophys. Res., 115,D21111, doi:10.1029/2009JD013568.

Porter, D., J. Cassano, and M. Serreze, 2012: Local and large-scale

atmospheric responses to reduced Arctic sea ice and ocean

warming in the WRF model. J. Geophys. Res., 117, D11115,

doi:10.1029/2011JD016969.

Rinke, A., K. Dethloff, W. Dorn, D. Handorf, and J. C. Moore,

2013: SimulatedArctic atmospheric feedbacks associated with

late summer sea ice anomalies. J. Geophys. Res. Atmos., 118,7698–7714, doi:10.1002/jgrd.50584.

Roeckner, E., and Coauthors, 2003: The atmospheric general cir-

culation model ECHAM5. Part I: Model description. Max

Planck Institute for Meteorology Rep. 349, 127 pp. [Available

from MPI for Meteorology, Bundesstr. 53, 20146 Hamburg,

Germany.]

Screen, J. A., and I. Simmonds, 2010: The central role of dimin-

ishing sea ice in recent Arctic temperature amplification.

Nature, 464, 1334–1337, doi:10.1038/nature09051.

——, C. Deser, I. Simmonds, and R. Tomas, 2014: Atmospheric

impacts of Arctic sea-ice loss, 1979–2009: Separating forced

change from atmospheric internal variability. Climate Dyn.,

43, 333–344, doi:10.1007/s00382-013-1830-9.

Takaya, K. and H. Nakamura, 2001: A formulation of a phase-

independent wave-activity flux for stationary and migratory

quasigeostrophic eddies on a zonally varying basic flow. J. At-

mos. Sci., 58, 608–627, doi:10.1175/1520-0469(2001)058,0608:

AFOAPI.2.0.CO;2.

Tang, Q., X. Zhang, X. Yang, and J. Francis, 2013: Cold winter ex-

tremes in northern continents linked to Arctic sea ice loss.

Environ. Res. Lett., 8, 014036, doi:10.1088/1748-9326/8/1/014036.

Vihma, T., 2014: Effects of Arctic sea ice decline on weather and

climate: A review. Surv. Geophys., 35, 1175–1214, doi:10.1007/

s10712-014-9284-0.

Walsh, J. E., 2014: Intensified warming of the Arctic: Causes and

impacts on middle latitudes. Global Planet. Change, 117, 52–63, doi:10.1016/j.gloplacha.2014.03.003.

Wu, B., and J.Wang, 2002:WinterArctic Oscillation, Siberian high

and East Asian winter monsoon.Geophys. Res. Lett., 29, 1897,

doi:10.1029/2002GL015373.

——, R. Huang, and D. Gao, 1999: Effects of variation of winter

sea-ice area in Kara and Barents Seas on East Asian winter

monsoon. Acta Meteor. Sin., 13, 141–153.

——, R. Zhang, and R. D’Arrigo, 2006: Distinct modes of the East

Asian winter monsoon. Mon. Wea. Rev., 134, 2165–2179,

doi:10.1175/MWR3150.1.

——, J. Su, and R. Zhang, 2011: Effects of autumn–winter Arctic

sea ice on winter Siberian high.Chin. Sci. Bull., 56, 3220–3228,

doi:10.1007/s11434-011-4696-4.

——, J. E. Overland, and R. D’Arrigo, 2012: Anomalous Arctic sur-

face wind patterns and their impacts on September sea ice min-

ima and trend. Tellus, 64, 18590, doi:10.3402/tellusa.v64i0.18590.

Yang, L.-N., and B. Wu, 2013: Interdecadal variations of the East

Asian winter surface air temperature and possible causes.

Chin. Sci. Bull., 58, 3969–3977, doi:10.1007/s11434-013-5911-2.

6858 JOURNAL OF CL IMATE VOLUME 28

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